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Search Results (1,332)

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12 pages, 3813 KiB  
Article
Ninoa T. cruzi Strain Modifies the Expression of microRNAs in Cardiac Tissue and Plasma During Chagas Disease Infection
by Rogelio F. Jiménez-Ortega, Ricardo Alejandre-Aguilar, Nancy Rivas, Fausto Sánchez, Fausto Sánchez-Muñoz and Martha A. Ballinas-Verdugo
Pathogens 2024, 13(12), 1127; https://doi.org/10.3390/pathogens13121127 - 20 Dec 2024
Abstract
Background: Chronic chagasic cardiomyopathy is the most severe clinical manifestation of Chagas disease, which affects approximately seven million people worldwide. Latin American countries bear the highest burden, with the greatest morbidity and mortality rates. Currently, diagnostic methods do not provide information on the [...] Read more.
Background: Chronic chagasic cardiomyopathy is the most severe clinical manifestation of Chagas disease, which affects approximately seven million people worldwide. Latin American countries bear the highest burden, with the greatest morbidity and mortality rates. Currently, diagnostic methods do not provide information on the risk of progression to severe stages of the disease. Recently, microRNAs (miRNAs) have been proposed as promising tools for monitoring the progression of Chagas disease. This study aimed to analyze the expression profiles of the miRNAs miR-1, miR-16, miR-208, and miR-208b in cardiac tissue, plasma, and plasma extracellular vesicles from Ninoa TcI-infected mice during the acute and indeterminate phases of Chagas disease. Methods: The cardiac-specific miRNAs and miR-16 levels were examined in all samples using RT-qPCR. Additionally, pathway analysis was performed to investigate the impact of potential miRNA target genes across various databases. Results: Elevated miR-208b expression was observed in cardiac tissue and plasma during the acute phase. Bioinformatic analysis identified three pathways implicated in disease progression: phosphatidylinositol 3-kinase signaling, Fc gamma receptor-mediated phagocytosis, and leukocyte transendothelial migration, as well as cholinergic synapse pathways. Conclusions: MiR-208b was upregulated during the acute phase and downregulated in the indeterminate phase, suggesting it may play a crucial role in disease progression. Full article
(This article belongs to the Special Issue Trypanosoma cruzi Infection: Cellular and Molecular Basis)
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<p>Infection and histology. (<b>a</b>) Histological section of regular mouse cardiac tissue. (<b>b</b>) Histological section of cardiac tissue during the acute phase. The presence of mature lymphocytes derived from amastigote nests is observed. (<b>c</b>) Histological section of cardiac tissue during the indeterminate phase. The arrow indicates amastigotes nests, and the square brackets highlight the inflammatory process.</p>
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<p>miRNA expression levels during the acute phase. (<b>a</b>) Expression levels in cardiac tissue infected with the Ninoa strain. (<b>b</b>) Expression levels in plasma from mice infected with the Ninoa strain. Cardiac tissue samples were normalized to U6, and plasma samples were normalized to cel-miR-39.</p>
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<p>miRNA expression levels during the indeterminate phase. (<b>a</b>) Expression levels in cardiac tissue infected with the Ninoa strain. (<b>b</b>) Expression levels in plasma from mice infected with the Ninoa strain. Cardiac tissue samples were normalized to U6, and plasma samples were normalized to cel-miR-39.</p>
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<p>miRNA expression levels in EVs. (<b>a</b>) Expression levels in plasma from mice infected with the Ninoa strain during the acute phase. (<b>b</b>) Expression levels in plasma from mice infected with the Ninoa strain during the indeterminate phase. Cardiac tissue samples were normalized to U6, and plasma samples were normalized to cel-miR-39.</p>
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<p>Bioinformatic analysis (<b>a</b>) Volcano plot showing underexpressed and overexpressed genes, with a fold-change less than −1 or greater than 1 and a <span class="html-italic">p</span>-value &lt; 0.05. (<b>b</b>) Venn diagram illustrating the overlap of putative target genes for miR-1, miR-16, miR-208, and miR-208b from humans and mice, along with differentially expressed genes from Mouse Genome 430A 2.0 microarrays from the Affymetrix platform. (<b>c</b>) Enrichment pathways identified using ShinyGO v0.741 software and the number of participating genes in each pathway.</p>
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<p>miRNA-Target gene interaction network. Interaction network of miR-1, miR-16, miR-208, and miR-208b with 26 potential target genes involved in enriched signaling pathways. These genes are associated with inflammatory processes and <span class="html-italic">T. cruzi</span> virulence. Key genes include Nuclear Factor of Activated T cells 1 (NFATc1), Nuclear Factor of Activated T cells 5 (NFAT5), cAMP Responsive Element Binding Protein 1 (CREB1), B-cell lymphoma 2 (BCL2), and Glycogen Synthase Kinase 3 beta (GSK3B).</p>
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16 pages, 1540 KiB  
Review
The Invertebrate Immunocyte: A Complex and Versatile Model for Immunological, Developmental, and Environmental Research
by Sandro Sacchi, Davide Malagoli and Nicola Franchi
Cells 2024, 13(24), 2106; https://doi.org/10.3390/cells13242106 - 19 Dec 2024
Abstract
The knowledge of comparative and developmental immunobiology has grown over the years and has been strengthened by the contributions of multi-omics research. High-performance microscopy, flow cytometry, scRNA sequencing, and the increased capacity to handle complex data introduced by machine learning have allowed the [...] Read more.
The knowledge of comparative and developmental immunobiology has grown over the years and has been strengthened by the contributions of multi-omics research. High-performance microscopy, flow cytometry, scRNA sequencing, and the increased capacity to handle complex data introduced by machine learning have allowed the uncovering of aspects of great complexity and diversity in invertebrate immunocytes, i.e., immune-related circulating cells, which until a few years ago could only be described in terms of morphology and basic cellular functions, such as phagocytosis or enzymatic activity. Today, invertebrate immunocytes are recognized as sophisticated biological entities, involved in host defense, stress response, wound healing, organ regeneration, but also in numerous functional aspects of organismal life not directly related to host defense, such as embryonic development, metamorphosis, and tissue homeostasis. The multiple functions of immunocytes do not always fit the description of invertebrate organisms as simplified biological systems compared to those represented by vertebrates. However, precisely the increasing complexity revealed by immunocytes makes invertebrate organisms increasingly suitable models for addressing biologically significant and specific questions, while continuing to present the undeniable advantages associated with their ethical and economic sustainability. Full article
(This article belongs to the Section Cellular Immunology)
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<p>The functional complexity of circulating invertebrate immunocytes. Morphological and molecular evidence has revealed that circulating hemocytes play numerous roles related to immunity and of non-immune-related processes, including development, stress response, wound repair, and regeneration.</p>
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<p>Immune-related molecules involved in homeostasis on neurons and nervous tissue. In phylogenetically distant models, immune-related cells and neurons have been shown to interact via common mediators. Originally discovered for their role in the pathogen-associated immune response, these soluble factors and cell-membrane receptors have subsequently been implicated in neuronal development (e.g., Dscam), synaptic pruning (e.g., complement system components), and immunocyte–neuron interactions (e.g., cytokines).</p>
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20 pages, 11527 KiB  
Article
Phosphoproteomic Profiling Reveals mTOR Signaling in Sustaining Macrophage Phagocytosis of Cancer Cells
by Bixin Wang, Xu Cao, Krystine Garcia-Mansfield, Jingkai Zhou, Antigoni Manousopoulou, Patrick Pirrotte, Yingyu Wang, Leo D. Wang and Mingye Feng
Cancers 2024, 16(24), 4238; https://doi.org/10.3390/cancers16244238 - 19 Dec 2024
Abstract
Background: Macrophage-mediated cancer cell phagocytosis has demonstrated considerable therapeutic potential. While the initiation of phagocytosis, facilitated by interactions between cancer cell surface signals and macrophage receptors, has been characterized, the mechanisms underlying its sustentation and attenuation post-initiation remain poorly understood. Methods: [...] Read more.
Background: Macrophage-mediated cancer cell phagocytosis has demonstrated considerable therapeutic potential. While the initiation of phagocytosis, facilitated by interactions between cancer cell surface signals and macrophage receptors, has been characterized, the mechanisms underlying its sustentation and attenuation post-initiation remain poorly understood. Methods: Through comprehensive phosphoproteomic profiling, we interrogated the temporal evolution of the phosphorylation profiles within macrophages during cancer cell phagocytosis. Results: Our findings reveal that activation of the mTOR pathway occurs following the initiation of phagocytosis and is crucial in sustaining phagocytosis of cancer cells. mTOR inhibition impaired the phagocytic capacity, but not affinity, of the macrophages toward the cancer cells by delaying phagosome maturation and impeding the transition between non-phagocytic and phagocytic states of macrophages. Conclusions: Our findings delineate the intricate landscape of macrophage phagocytosis and highlight the pivotal role of the mTOR pathway in mediating this process, offering valuable mechanistic insights for therapeutic interventions. Full article
(This article belongs to the Special Issue Macrophage-Directed Cancer Immunotherapy)
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<p>A multiplexed quantitative analysis of the phosphoproteome was applied to analyzing phagocytosis of cancer cells by macrophages. (<b>A</b>) A schematic showing the design of the phosphoproteomic profiling. BMDMs were co-cultured with Raji cells, and the Raji cells were removed 0, 1, 15, 60, or 120 min after the co-culture. The macrophages were then lysed, and total proteins were digested into peptides and labeled with tandem mass tags (TMTs) before being pooled. The samples were then subjected to SMOAC phospho-enrichment, and phosphopeptides were detected using an Orbitrap Fusion Lumos Tribrid MS. (<b>B</b>) Null comparisons of the phosphoproteome datasets (0 min-R1 vs. 0 min-R2 and 120 min-R1 vs. 120 min-R2) (<b>left</b>) show significant differences in the patterns when contrasted with the true comparisons (120 min-R1 vs. 0 min-R1 and 120 min-R2 vs. 0 min-R2) (<b>right</b>). Each dot represents a phosphosite, with the colors indicating the log2 fold change ratio (y_ratio). The correlation coefficient (r) for each comparison is provided, with R signifying a replicate. (<b>C</b>) Partial least squares–discriminant analysis (PLS-DA) showing clustering of the time point replicates from the macrophage co-culture from the 3060 significant DA phosphosites. (<b>D</b>) Heatmap showing unsupervised clustering of the DA phosphosites, detected via a time series analysis using a linear model through limma. Clustering was calculated using Pearson’s correlation coefficient. The <span class="html-italic">x</span>-axis indicates the time points (0, 1, 15, 60, and 120 min), with the replicates for each condition. The <span class="html-italic">y</span>-axis represents individual phosphosites, grouped by the similarity in their abundance patterns throughout the time series, while the color gradient depicts the z-scores (scaled phosphosite abundance).</p>
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<p>Phosphoproteome profiling identified the activation of the mTOR pathway during macrophage phagocytosis. (<b>A</b>) Top 20 pathways with differentially abundant phosphosites according to KEGG pathway analysis. The circle size represents the count of differentially abundant phosphosites, and the color indicates the adjusted <span class="html-italic">p</span>-value (p.adjust). The <span class="html-italic">x</span>-axis displays the pathway names, while the <span class="html-italic">y</span>-axis represents the GeneRatio (the proportion of genes in each pathway relative to the total number of genes analyzed). (<b>B</b>) Clusters of phosphosites with similar trends were identified using a DEGPattern analysis. Only patterns with at least 50 phosphosites were retained, resulting in 7 final clusters. (<b>C</b>) Dot plots of significantly enriched pathways for each cluster (except cluster 7, which reported no significant pathways). The dot color indicates the <span class="html-italic">p</span>-value of enrichment, while the circle size represents the ratio of genes from the pathway present in each cluster. The <span class="html-italic">x</span>-axis displays the pathway names, while the <span class="html-italic">y</span>-axis represents the cluster numbers. (<b>D</b>,<b>E</b>) UMAP plots showing immune cell clusters of colorectal and breast tumors (GSE180296 and GSE139492). (<b>D</b>) The cluster includes myeloid cells, NK cells, DCs, neutrophils, and CD8, B, and CD4 cells. (<b>E</b>) The cluster includes DCs, neutrophils, and myeloid, T/NK, tumor, and B cells. (<b>F</b>,<b>G</b>) The bar plot illustrates the average mTOR pathway activity (mTOR_Score) across different cell types. (<b>H</b>) Rapamycin treatment significantly inhibited CD47-blockade-induced phagocytosis of the Raji cells (200 nM, 40 nM, 8 nM, and 1.6 nM rapamycin) by the BMDMs. N = 3. (<b>I</b>) Rapamycin pretreatment (100 nM) significantly inhibited phagocytosis of the Raji cells by the BMDMs at different E:T ratios. N = 3. (<b>J</b>) Rapamycin treatment (100 nM) significantly inhibited CD47-blockade-induced phagocytosis of the Raji cells at different E:T ratios by human peripheral blood monocyte-derived macrophages. N = 3. Data are represented as means ± SDs. NS indicates not statistically significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, as determined by one-way ANOVA (H) or two-way ANOVA (I, J).</p>
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<p>Blockade of the mTOR pathway shows no impact on the cell surface receptor expression or polarization states of the macrophages. (<b>A</b>) Cell viability of the macrophages was analyzed at 24 h with or without 100 nM rapamycin treatment. N = 3. (<b>B</b>) Representative FACS plots showing the expression of FcүRI, FcүRIIB, FcүRIII, and FcүRIV on the BMDMs with or without 100 nM rapamycin treatment for 24 h. N = 3. (<b>C</b>) Representative FACS plots showing the expression of SIRPα on the BMDMs with or without 100 nM rapamycin treatment for 24 h. N = 3. (<b>D</b>) Representative FACS plots showing the expression of F4/80, CD80, CD86, iNOS, MHC II, and CD206 on the BMDMs with or without 100 nM rapamycin pretreatment for 24 h. N = 3. Data are represented as means ± SD. NS indicates not statistically significant as determined using an unpaired t-test (<b>A</b>).</p>
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<p>Blockade of mTOR signaling impairs the capacity of macrophages for phagocytosis but not their affinity with it. (<b>A</b>–<b>C</b>) A flow-cytometry-based phagocytosis assay. CFSE-labeled Raji cells were incubated with BMDMs treated with control vehicle or rapamycin in the presence of 0.02, 0.2, or 2 µg/mL of CD47-blocking antibody. Representative FACS plots showing the expression of F4/80<sup>+</sup>CFSE<sup>+</sup> cells at 2 h, 6 h, 12 h, and 24 h in the control group and the rapamycin group. The percentages of F4/80<sup>+</sup>CFSE<sup>+</sup> cells at different time points were summarized for all three groups. N = 3. (<b>D</b>) A luminescence-based phagocytosis assay. Raji cells were incubated with BMDMs treated with control vehicle or rapamycin in the presence of 0, 0.02, or 0.2 µg/mL of CD47-blocking antibody. Surviving Raji cells were quantified using luminescence. N = 3. Data are represented as means ± SDs. NS indicates not statistically significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, as determined using two-way ANOVA (<b>A</b>–<b>D</b>).</p>
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<p>Blockade of mTOR signaling delays phagosome maturation and compromises non-phagocytic macrophages’ transition into a phagocytic state with cancer cell rechallenge. (<b>A</b>,<b>B</b>) LysoTracker staining of the BMDMs after co-culture with the Raji cells in the presence of 0.02 or 0.2 µg/mL of CD47-blocking antibody. BMDMs were treated with control vehicle or rapamycin. (<b>A</b>) Representative FACS plots. The cells were assessed using FACS to quantify the intensity of the lysosome acidity of the F4/80<sup>+</sup>CSFE<sup>+</sup> and F4/80<sup>+</sup>CSFE<sup>−</sup> populations. (<b>B</b>) Summary of the Mean Fluorescence Intensity (MFI) of LysoTracker gated from the F4/80<sup>+</sup>CSFE<sup>+</sup> and F4/80<sup>+</sup>CSFE<sup>−</sup> cells. N = 3. (<b>C</b>,<b>D</b>) BMDMs were co-cultured with CFSE-labeled Raji cells and rechallenged with Far Red-labeled Raji cells at 6 h or 12 h and then harvested at 24 h in the presence of 0.02 or 0.2 µg/mL of CD47 blocking antibody. (<b>C</b>) Representative FACS plots show the percentage of the F4/80<sup>+</sup>APC<sup>+</sup> population gated from the F4/80<sup>+</sup>CSFE<sup>+</sup> or F4/80<sup>+</sup>CSFE<sup>−</sup> population and a summary of the percentages of the F4/80<sup>+</sup>CSFE<sup>+</sup>, F4/80<sup>+</sup>CSFE<sup>−</sup>, F4/80<sup>+</sup>CSFE<sup>+</sup>APC<sup>+</sup>, and F4/80<sup>+</sup>CSFE<sup>−</sup>APC<sup>+</sup> populations in the 6 h (<b>C</b>) and 12 h rechallenge experiments (<b>D</b>). N = 3. Data represented as means ± SDs. NS indicates not statistically significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001, as determined using two-way ANOVA (<b>B</b>–<b>D</b>).</p>
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17 pages, 4442 KiB  
Article
Pichia pastoris-Derived β-Glucan Capsules as a Delivery System for DNA Vaccines
by Samara Sousa de Pinho, Maria da Conceição Viana Invenção, Anna Jéssica Duarte Silva, Larissa Silva de Macêdo, Benigno Cristofer Flores Espinoza, Lígia Rosa Sales Leal, Marco Antonio Turiah Machado da Gama, Ingrid Andrêssa de Moura, Micaela Evellin dos Santos Silva, Débora Vitória Santos de Souza, Marina Linhares Lara, Julia Nayane Soares Azevedo Alves and Antonio Carlos de Freitas
Vaccines 2024, 12(12), 1428; https://doi.org/10.3390/vaccines12121428 - 18 Dec 2024
Viewed by 277
Abstract
Background/Objectives: DNA vaccines are rapidly produced and adaptable to different pathogens, but they face considerable challenges regarding stability and delivery to the cellular target. Thus, effective delivery methods are essential for the success of these vaccines. Here, we evaluated the efficacy of capsules [...] Read more.
Background/Objectives: DNA vaccines are rapidly produced and adaptable to different pathogens, but they face considerable challenges regarding stability and delivery to the cellular target. Thus, effective delivery methods are essential for the success of these vaccines. Here, we evaluated the efficacy of capsules derived from the cell wall of the yeast Pichia pastoris as a delivery system for DNA vaccines. Methods: The capsules were extracted from the yeast Pichia pastoris strain GS115, previously grown in a YPD medium. pVAX1 expression vector was adopted to evaluate the DNA vaccine insertion and delivery. Three encapsulation protocols were tested to identify the most effective in internalizing the plasmid. The presence of plasmids inside the capsules was confirmed by fluorescence microscopy, and the encapsulation efficiency was calculated by the difference between the initial concentration of DNA used for insertion and the concentration of unencapsulated DNA contained in the supernatant. The capsules were subjected to different temperatures to evaluate their thermostability and were co-cultured with macrophages for phagocytosis analysis. HEK-293T cells were adopted to assess the cytotoxicity levels by MTT assay. Results: The microscopy results indicated that the macrophages successfully phagocytosed the capsules. Among the protocols tested for encapsulation, the one with 2% polyethylenimine for internalization showed the highest efficiency, with an encapsulation rate above 80%. However, the vaccine capsules obtained with the protocol that used 5% NaCl showed better thermal stability and encapsulation efficiency above 63% without induction of cell viability loss in HEK 293T. Conclusions: We successfully described a vaccine delivery system using yeast capsules derived from Pichia pastoris, demonstrating its potential for DNA vaccine delivery for the first time. Additional studies will be needed to characterize and improve this delivery strategy. Full article
(This article belongs to the Special Issue Advance in Nanoparticles as Vaccine Adjuvants)
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<p>Schematic representation of the methodology used for capsule preparation. This figure outlines the process of yeast cell treatment and preparation. In (<b>A</b>), the whole yeast cells are cultured, providing the starting material for the yeast capsule procedure; after that, the cells undergo a chemical treatment (<b>B</b>), first with NaOH (1 M) at 60 °C for 1 h to disrupt cell components, followed by HCl (pH 4.5) at 55 °C for 1 h to further process and clean the cellular material. The treated material is subjected to multiple washes (<b>C</b>), including four rounds with isopropanol and two with acetone, to remove impurities and ensure purity of the capsules. The final capsules are dried at room temperature (RT) and stored at −20 °C to maintain their integrity and stability for further applications (<b>D</b>).</p>
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<p>Methodologies for inserting plasmid DNA into the capsules. (<b>A</b>) Insertion methodology 1 (PI-1) using 2% PEI; (<b>B</b>) insertion methodology 2 (PI-2) using 5% NaCl. Grey arrows represents eletrostatic interactions between DNA and PEI/NaCl.</p>
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<p>Optical microscopy of <span class="html-italic">Pichia pastoris</span> GS115 and yeast shells. Samples were stained with methylene blue and observed at 40× and 100× magnification. Microscopy with K55 OIT optical microscope (Kasvi, Pinhais, PR, Brazil) and image manual capture system.</p>
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<p>Comparison of the plasmid DNA incorporation efficiency between the three protocols adopted. %EE = encapsulation efficiency percentage. Different DNA concentrations were adopted to verify the loading capacity using 50, 250, and 500 ng/µL (x-axis). Values correspond to mean ± standard deviation.</p>
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<p>Fluorescence microscopy of capsules yeasts. YS: Empty capsules; YS + DNA + PEI 2%; and YS + DNA + NaCl 5%. Observation of the labeling of YS with DAPI (blue), indicating the presence of nucleic acid in the capsules. YS was used as a negative expression control and displayed only fluorescence background. Images captured through a Leica DMLB epi-fluorescence microscope (Leica, Wetzlar, Alemanha) at 100× magnification. Images were captured by the Leica DFC345 FX. Scale bar: 5 μm.</p>
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<p>Evaluation of the maintenance of plasmid DNA inside the capsules under different heat treatments. (<b>A</b>) PEI 2% and (<b>B</b>) NaCl 5% were the compounds used to incorporate the DNA inside the capsules. The y-axis corresponds to the DNA concentration, and the x-axis corresponds to the temperatures that were tested. Bar = means ± SD. Asterisks represent statistical significance (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Phagocytosis verification by fluorescence microscopy. Groups: THP-1 cells only and THP-1 + YS (yeast shell), observed under bright-field and fluorescence microscopy. YS were labeled with 5′DTAF (green), and THP-1 cells were labeled with DAPI (blue). The white arrows highlight the YS phagocytosed by the macrophages. Images were captured through a fluorescence microscope (Motic AE31E) at 40× magnification. Images were captured by the Moticam S6 camera using Motic Images Plus 3.0 software. Scale bar: 5 μm.</p>
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<p>Cell viability evaluation by MTT assay. HEK 293-T cells were treated by incubation with yeast shells (capsules). Asterisks represent statistical significance (**** <span class="html-italic">p</span> &lt; 0.0001), determined by the variance test (ANOVA). Values correspond to mean ± standard deviation.</p>
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<p>Comparison of DNAse assay results. (<b>A</b>) Empty yeast capsules and capsules with two insertion protocols (PEI 2% and NaCl 5%) whose inserted DNA (pVAX1) was stained with DAPI (blue color) without being subjected to DNAse treatment. (<b>B</b>) Empty yeast capsules and capsules with two insertion protocols with DNA stained with DAPI but subjected to DNAse treatment. It was possible to observe that there was no degradation of the DNA inserted in the capsules, even in medium containing DNAse. Images captured through a fluorescence microscope (Motic AE31E) at 40× magnification. Images were captured by the Moticam S6 camera using Motic Images Plus 3.0 software.</p>
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14 pages, 7883 KiB  
Article
Immunoregulatory Effects of Codonopsis pilosula Polysaccharide Modified Selenium Nanoparticles on H22 Tumor-Bearing Mice
by Yan Long, Hongfei Ji, Jiajing Yang, Haiyu Ji, Keyao Dai, Wenjie Ding, Guoqiang Zheng and Juan Yu
Foods 2024, 13(24), 4073; https://doi.org/10.3390/foods13244073 - 17 Dec 2024
Viewed by 279
Abstract
Codonopsis pilosula polysaccharide (CPP) and rare element selenium (Se) have been proved to exert various biological activities, and our previous study demonstrated that selenium nanoparticles modified with CPP (CPP-SeNPs) possessed significantly enhanced tumor cytotoxicity in vitro. This study aimed to investigated the inhibitory [...] Read more.
Codonopsis pilosula polysaccharide (CPP) and rare element selenium (Se) have been proved to exert various biological activities, and our previous study demonstrated that selenium nanoparticles modified with CPP (CPP-SeNPs) possessed significantly enhanced tumor cytotoxicity in vitro. This study aimed to investigated the inhibitory effects of CPP-SeNPs complex on H22 solid tumors via immune enhancement. In this study, the H22 tumor-bearing mice model was constructed, and the potential mechanisms of CPP-SeNPs antitumor effects were further explored by evaluating cytokines expression levels, immune cells activities and tumor cells apoptotic indicators in each group. The results demonstrated that CPP-SeNPs effectively exerted dose-dependent protective effects on the immune organs of tumor-bearing mice in vivo, leading to increase in peripheral white blood cell counts and inhibition of solid tumor growth with inhibitory rate of 47.18% in high-dose group (1.5 mg/kg). Furthermore, CPP-SeNPs treatment significantly elevated the levels of TNF-α, IFN-γ, and IL-2 in mice sera, enhanced NK cell cytotoxicity, augmented macrophage phagocytosis capacity, as well as increased both the amounts and proliferation activity of lymphocyte subsets. CPP-SeNPs improved the immune system’s ability to clear tumor cells by up-regulating Bax expression while down-regulating Bcl-2 expression within solid tumors, indicating the potential activation of mitochondrial apoptosis pathway. Therefore, CPP-SeNPs administration can effectively inhibit tumor growth by enhancing immune response in tumor-bearing mice, which might be relevant to the regulation of gut microbiota short-chain fatty acids metabolisms. These findings could provide theoretical support and data foundation for further development of CPP-SeNPs as functional food and drug adjuvants. Full article
(This article belongs to the Section Nutraceuticals, Functional Foods, and Novel Foods)
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<p>CPP-SeNP synthesis process (<b>A</b>) and animal experiment arrangement (<b>B</b>).</p>
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<p>Effects of CPP-SeNPs on thymus indices (<b>A</b>), spleen indices (<b>B</b>), weight gain (<b>C</b>), and tumor weights (<b>D</b>) of H22-bearing mice. Note: <sup>#</sup>, <span class="html-italic">p</span> &lt; 0.05 compared with blank group; *, <span class="html-italic">p</span> &lt; 0.05 compared with model group.</p>
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<p>Effects of CPP-SeNPs on peripheral blood leukocytes (<b>A</b>), and IL-2 (<b>B</b>), TNF-α (<b>C</b>), and INF-γ (<b>D</b>) levels of H22-bearing mice. Note: <sup>#</sup>, <span class="html-italic">p</span> &lt; 0.05 compared with blank group; *, <span class="html-italic">p</span> &lt; 0.05 compared with model group.</p>
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<p>Effects of CPP-SeNPs on proliferative capacity of splenic B (<b>A</b>) and T (<b>B</b>) lymphocytes, killing capacity of splenic NK cells (<b>C</b>) and peritoneal macrophages phagocytosis (<b>D</b>) in mice. Note: <sup>#</sup>, <span class="html-italic">p</span> &lt; 0.05 compared with blank group; *, <span class="html-italic">p</span> &lt; 0.05 compared with model group.</p>
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<p>Effects of CPP-SeNPs on the distributions and proportions of lymphocytes subsets in peripheral blood. (<b>A</b>) CD3<sup>+</sup> T cells and CD19<sup>+</sup> B cells distributions; (<b>B</b>) CD4<sup>+</sup> T cells and CD8<sup>+</sup> T cells distributions; (<b>C</b>) CD3<sup>+</sup> T cells and CD19<sup>+</sup> B cells proportions; (<b>D</b>) CD4<sup>+</sup> T cells and CD8<sup>+</sup> T cells proportions. Note: <sup>#</sup>, <span class="html-italic">p</span> &lt; 0.05 compared with blank group; *, <span class="html-italic">p</span> &lt; 0.05 compared with model group.</p>
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<p>Effects of CPP-SeNPs administration on solid tumor cells condition. (<b>A</b>) H&amp;E staining results; (<b>B</b>) western blot determination; (<b>C</b>) Bax or Bcl-2 expression ratios to β-actin. Note: *, <span class="html-italic">p</span> &lt; 0.05 compared with model group.</p>
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20 pages, 7124 KiB  
Article
Distinct UPR and Autophagic Functions Define Cell-Specific Responses to Proteotoxic Stress in Microglial and Neuronal Cell Lines
by Helena Domínguez-Martín, Elena Gavilán, Celia Parrado, Miguel A. Burguillos, Paula Daza and Diego Ruano
Cells 2024, 13(24), 2069; https://doi.org/10.3390/cells13242069 - 15 Dec 2024
Viewed by 420
Abstract
Autophagy is a catabolic process involved in different cellular functions. However, the molecular pathways governing its potential roles in different cell types remain poorly understood. We investigated the role of autophagy in the context of proteotoxic stress in two central nervous system cell [...] Read more.
Autophagy is a catabolic process involved in different cellular functions. However, the molecular pathways governing its potential roles in different cell types remain poorly understood. We investigated the role of autophagy in the context of proteotoxic stress in two central nervous system cell types: the microglia-like cell line BV2 and the neuronal-like cell line N2a. Proteotoxic stress, induced by proteasome inhibition, produced early apoptosis in BV2 cells, due in part to a predominant activation of the PERK-CHOP pathway. In contrast, N2a cells showcased greater resistance and robust induction of the IRE1α-sXbp1 arm of the UPR. We also demonstrated that proteotoxic stress activated autophagy in both cell lines but with different kinetics and cellular functions. In N2a cells, autophagy restored cellular proteostasis, while in BV2 cells, it participated in regulating phagocytosis. Finally, proteotoxic stress predominantly activated the mTORC2-AKT-FOXO1-β-catenin pathway in BV2 cells, while N2a cells preferentially induced the PDK1-AKT-FOXO3 axis. Collectively, our findings suggest that proteotoxic stress triggers cell-specific responses in microglia and neurons, with different physiological outcomes. Full article
(This article belongs to the Special Issue Understanding the Interplay Between Autophagy and Neurodegeneration)
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<p><b>Analysis of cell viability and apoptosis following proteasome inhibition</b>. (<b>A</b>) Measure of apoptosis by flow cytometry after 6 h of MG132 treatment, using the combination of Annexin V-FITC/propidium iodide staining in BV2 and N2a cells. (<b>B</b>) Analysis of cell survival following 1, 2, 4, 6, and 8 h of MG132 incubation in both cell lines. (<b>C</b>) Representative image of the western blot of cleaved caspase-3 at the different time points. Bands correspond to 17 and 19 kDa cleaved fragments. No expression detected in N2a cells. Data are expressed as means of the percentage of cell survival ± SD. Cell viability assay was performed at least four times. Statistical significance ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p><b>UPR markers expression</b>. Quantification of the transcriptional expression (left), representative western blots (middle), and protein levels (right) of the UPR downstream markers Grp78 (<b>A</b>), sXbp1 (<b>B</b>), and CHOP (<b>C</b>), following MG132 treatment at different time points in both cell lines. Actin is included as loading control. Data are expressed as arbitrary units of fold change in the gene expression and as mean of the percentage of the optical density (OD) normalized to control for the protein expression. Experiments were repeated at least three times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p><b>Assessment of the implication of IRE1α and PERK branches on cell survival</b>. (<b>A</b>) Correlation between the percentage of cell survival and the protein CHOP/sXBP1 ratio in BV2 (left) and N2a (right) cells following MG132 treatment. (<b>B</b>) Analysis of cell survival following 6 h of simultaneous inhibition of IRE1α and PERK in presence and absence of MG132. Data are expressed as mean of the percentage of cell survival or the ratio of OD ± SD. Experiments of cell survival were repeated at least five times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Evaluation of autophagic markers following proteasome inhibition in both cell lines. (<b>A</b>) Representative images of western blots of the autophagic marker LC3-II (and actin as loading control) following MG132 treatment at different time points (upper panel). Quantification of LC3-II expression normalized to each control (lower panel). (<b>B</b>) Quantification of the transcriptional expression of p62 gene following proteasome inhibition. (<b>C</b>) Representative images of western blot of p62 (upper panel), P-p62-(S405) (middle panel), and actin as loading control after different time points of MG132 incubation. Below is shown the quantification of the percentage of p62 protein level (normalized to control) after different times of MG132 treatment. (<b>D</b>) The same for P-p62-(S405). (<b>E</b>) Representative western blot of LC3-II (upper panel), P-p62(S405) (middle panel), and actin as loading control, in the presence of MG132 (MG) and MG132 + bafilomycin (M + B). Relative intensity of the LC3-II band is indicated at the bottom of the gel, normalized to MG condition. (<b>F</b>) Quantification of the LC3-II/P-p62-(S405) ratio after 5 h of MG132 incubation, with or without bafilomycin in BV2 and N2a cell lines. Data are expressed as (i) means of the percentage of the optical density (OD) normalized to control for the protein expression; (ii) means of the arbitrary units of fold change in the gene expression; and (iii) means of the LC3-II/P-p62-(S405) ratio. Experiments were repeated at least three times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Analysis of polyubiquitinated protein accumulation and the effect of autophagy inhibition on cell survival and phagocytic activity following MG132 treatment. (<b>A</b>) Representative western blot of accumulated polyubiquitinated proteins following proteasome inhibition. (<b>B</b>) Quantification of the total amount of polyubiquitinated proteins showing accumulation in BV2 but not in N2a cells. Note the absence of high-molecular-weight polyubiquitinated proteins within N2a cells. (<b>C</b>) Quantification of the percentage of cell survival following the combined treatment with MG132 and the autophagy inhibitor 3-MA. (<b>D</b>) Representative scatter plot of flow cytometry analysis in BV2 cells. The <span class="html-italic">Y</span>-axis (PB450) corresponds to Hoechst staining, and the <span class="html-italic">X</span>-axis corresponds to 734 TAMRA staining. The upper plots are non-stained (NS) cells (left) and cells stained only with Hoechst (right). The upper right quadrant represents phagocytic cells (positive for both Hoechst and TAMRA). (<b>E</b>) Quantification of the percentage of phagocytic cells treated with MG132, 3-MA, or the combined treatment for 6 h (3-MA was pre-incubated 1 h before MG132). Experiments were repeated at least three times and five times for the phagocytosis assay. Data are expressed as means of the percentage normalized to control. Statistical significance ** (related to control) or ## (related to MG132 alone) <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Analysis of mTORC1, ULK1, and Akt phosphorylation levels following proteasome inhibition. (<b>A</b>) Representative western blot images of the evolution of P(S2448)-mTOR (upper panel) and P(S757)-ULK1 (middle panel) following proteasome inhibition. Actin was included as loading control. (<b>B</b>) Representative western blot images of P(S473)-Akt (upper panel), P(S308)-Akt (middle panel), and total Akt levels (lower panel). Data shown in the graphs are expressed as the mean of the percentage of the optical density (OD) normalized to control. Note the cell-specific difference in the profile of Akt phosphorylation. Experiments were repeated at least three times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Evaluation of GSK-3β, FOXO1, and FOXO3 phosphorylation levels following proteasome inhibition. (<b>A</b>) Representative western blot images of P(S9)-GSK-3β, total GSK-3β, and actin as loading control, following proteasome inhibition. The graph shows the quantification of P(S9)-GSK-3β/GSK-3β ratio. (<b>B</b>) Representative western blot images of P(S256)-FOXO1, FOXO1, and graphs showing the quantification of P(S256)-FOXO1 and FOXO1 following proteasome inhibition. (<b>C</b>) The same as in (<b>B</b>) but for P(S253)-FOXO3 and FOXO3. Actin was included as loading control. The data are expressed as the mean percentage of the ratio of the optical density (OD) normalized to the control. Experiments were repeated at least three times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Expression and phosphorylation of β-catenin following proteasome inhibition. Representative western blot images of P(S33/37/T41)-β-catenin and β-catenin following proteasome inhibition. Actin was included as loading control. Quantification of the western blots showing a differential dynamic in the level of both P(S33/37/T41)-β-catenin (middle graph) and β-catenin (lower graph). Experiments were repeated four times. Statistical significance * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Summary of the cell-specific responses to proteasome inhibition observed in BV2 and N2a cells. In BV2 cells, proteasome inhibition (PI) weakly activates the IRE1α-sXbp1 arm but predominantly triggers the pro-apoptotic PERK-CHOP pathway. Additionally, there is a prominent induction of the mTORC2-AKT-FOXO1-β-catenin pathway, potentially leading to LC3-associated phagocytosis (LAP) and subsequent cell death, which probably stimulates phagocytosis, leading to additional cell death. In N2a cells, PI activates the canonical UPR with predominance of the IRE1α-sXbp1 arm. Simultaneously, it triggers the PDK1-AKT-FOXO3 axis, stimulating autophagy for proteostasis restoration. According to present data, we propose that autophagy appears to play a role in restoring cellular proteostasis in neurons while regulating phagocytosis in microglial cells, likely mediated by differential Akt and p62 phosphorylation levels leading finally to cell survival or cell death.</p>
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27 pages, 2646 KiB  
Review
Role of NRF2 in Pathogenesis of Alzheimer’s Disease
by Ching-Tung Chu, Akira Uruno, Fumiki Katsuoka and Masayuki Yamamoto
Antioxidants 2024, 13(12), 1529; https://doi.org/10.3390/antiox13121529 - 13 Dec 2024
Viewed by 347
Abstract
Alzheimer’s disease (AD) is a polygenic, multifactorial neurodegenerative disorder and remains the most prevalent form of dementia, globally. Despite decades of research efforts, there is still no effective cure for this debilitating condition. AD research has increasingly focused on transcription factor NRF2 (nuclear [...] Read more.
Alzheimer’s disease (AD) is a polygenic, multifactorial neurodegenerative disorder and remains the most prevalent form of dementia, globally. Despite decades of research efforts, there is still no effective cure for this debilitating condition. AD research has increasingly focused on transcription factor NRF2 (nuclear factor erythroid 2-related factor 2) as a potential therapeutic target. NRF2 plays a crucial role in protecting cells and tissues from environmental stressors, such as electrophiles and reactive oxygen species. Recently, an increasing number of studies have demonstrated that NRF2 is a key regulator in AD pathology. NRF2 is highly expressed in microglia, resident macrophages in the central nervous system, and contributes to neuroinflammation, phagocytosis and neurodegeneration in AD. NRF2 has been reported to modulate microglia-induced inflammation and facilitate the transition from homeostatic microglia to a disease-associated microglia subset. Genetic and pharmacological activation of NRF2 has been demonstrated to improve cognitive function. Here, we review the current understanding of the involvement of NRF2 in AD and the critical role that NRF2 plays in microglia in the context of AD. Our aim is to highlight the potential of targeting NRF2 in the microglia as a promising therapeutic strategy for mitigating the progression of AD. Full article
(This article belongs to the Special Issue Role of NRF2 Pathway in Neurodegenerative Diseases)
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<p>Domain architectures of NRF2 and KEAP1. (<b>A</b>). NRF2 (nuclear actor erythroid 2-related factor 2) consists of seven domains, referred to as Neh1 to Neh7, each identified based on their specific biological functions and their homology to other protein domains. Neh stands for Nrf2-Ech homology domain. These domains play the crucial regulatory roles of NRF2 in cellular defense mechanisms, including oxidative stress response and detoxification processes, and anti-inflammation. (<b>B</b>). KEAP1 (Kelch-like ECH-associated protein) is composed of five domains. BTB domain and DGR domain are protein–protein interaction domains, separated by an intervening region (IVR). The BTB domain regulates homodimerization of KEAP1 and binding to CUL3 (Cullin3), while the DGR domain and the C-terminal region facilitate the binding with Neh2 domain of NRF2.</p>
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<p>Schematic presentation of the KEAP1-NRF2 system. Under normal conditions (black arrows), NRF2 is bound to KEAP1 in the cytoplasm. KEAP1 targets NRF2 for ubiquitination, leading to its degradation via the proteasomal pathway. Upon exposure to oxidative stress or NRF2 inducers (orange arrows), the KEAP1 undergoes modifications that reduce its ubiquitin ligase activity, resulting in weakening of KEAP1 for ubiquitinating NRF2. Subsequently, newly synthesized NRF2 translocates to the nucleus. In the nucleus, NRF2 binds to CNC-sMaf-binding elements (CsMBEs) or antioxidant response elements (AREs) in the promoter/enhancer regions of target genes. This binding activates the transcription of genes involved in the synthesis of reduced glutathione (GSH), antioxidant defense, detoxification processes, iron metabolism and inflammation.</p>
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<p>Expression levels of NRF2 in three major central nervous system (CNS) cell types. Microglia show high NRF2 expression, astrocytes exhibit medium NRF2 expression, and neurons display low NRF2 expression.</p>
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<p>NRF2 regulation of microglial states in AD. The downregulation of NRF2 in AD microglia enhances the expression of neurodegenerative phenotype markers, such as <span class="html-italic">CD11b</span>, <span class="html-italic">CD86</span>, and <span class="html-italic">iNOS</span>. This phenomenon has been observed in AD mini-brains, AD mouse models, and AD patients [<a href="#B190-antioxidants-13-01529" class="html-bibr">190</a>]. Conversely, the activation of NRF2 in AD mice leads to a reduction in the expression of disease-associated microglia (DAM) markers, including <span class="html-italic">TREM2</span>, <span class="html-italic">TYROBP</span>, <span class="html-italic">CST7</span>, and <span class="html-italic">ITGAX</span> [<a href="#B76-antioxidants-13-01529" class="html-bibr">76</a>]. These findings collectively underscore the crucial role of NRF2 in regulating the phenotypic transition of microglia within the context of AD, suggesting that enhancing NRF2 signaling could be a potential therapeutic strategy to modulate neuroinflammation and improve outcomes in AD.</p>
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<p>Schematic representation of NRF2-mediated neuroprotection and ferroptosis in AD. The relationship between ferroptosis and AD pathologies is increasingly being recognized. Activation of the NRF2 pathway is associated with neuroprotection in the brain, primarily through the upregulation of antioxidant genes such as GPX4 and NQO1, as well as genes involved in iron metabolism. Oxidative stress and iron deposition in the brain can promote ferroptosis, which exacerbates AD pathologies. The correlation between NRF2 activation and reduced ferroptosis activity suggests that NRF2 may serve as a potential therapeutic target for mitigating neurodegeneration in AD, in part by preventing ferroptosis.</p>
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<p>Schematic representation of NRF2 functions in CNS. (<b>A</b>) Cell type-specific functions of NRF2 in CNS. Diverse roles of NRF2 in three distinct cell types within the CNS and vasculature are shown. Note that in microglia, NRF2 reduces inflammation, enhances antioxidant defenses, and regulates phagocytosis, contributing to immune homeostasis and neuroprotection. In astrocytes, NRF2 promotes glutathione (GSH) production, alleviates metabolic stress, and reduces inflammation, supporting neuronal survival and CNS health. In neurons, NRF2 improves mitochondrial function and resistance to oxidative damage, protecting against neurodegeneration. In the vasculature, NRF2 enhances endothelial barrier function and mitigates inflammatory responses, maintaining vascular integrity and reducing neurovascular dysfunction. Collectively, NRF2 serves as a central regulator of oxidative stress and inflammation across multiple CNS cell types and the vascular system, highlighting its therapeutic potential in neurodegenerative diseases such as AD. (<b>B</b>) Schematic representation of NRF2 regulation in the CNS. When NRF2 activated in the CNS, NRF2 regulates the activation of microglia and astrocytes, leading to a decrease in inflammatory responses and a reduction in oxidative stress. Additionally, NRF2 plays a vital role in maintaining iron homeostasis, enhancing mitochondrial function and protection of cerebrovascular health. Together, these effects of NRF2 activation underscore its significant influence in mitigating AD pathology by modulating cellular responses and processes.</p>
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12 pages, 1482 KiB  
Article
Bovine Lactoferrin Enhances Toll-like Receptor 7 Response in Plasmacytoid Dendritic Cells and Modulates Cellular Immunity
by Takumi Yago, Asuka Tada, Shutaro Kubo, Hirotsugu Oda, Sadahiro Iwabuchi, Miyuki Tanaka and Shinichi Hashimoto
Int. J. Mol. Sci. 2024, 25(24), 13369; https://doi.org/10.3390/ijms252413369 - 13 Dec 2024
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Abstract
Plasmacytoid dendritic cells (pDCs) express Toll-like receptor 7 (TLR7) in the endosomes, recognize viral single-stranded RNA (ssRNA), and produce significant amounts of interferon (IFN)-α. Bovine lactoferrin (LF) enhances the response of IFN regulatory factors followed by the activation of IFN-sensitive response elements located [...] Read more.
Plasmacytoid dendritic cells (pDCs) express Toll-like receptor 7 (TLR7) in the endosomes, recognize viral single-stranded RNA (ssRNA), and produce significant amounts of interferon (IFN)-α. Bovine lactoferrin (LF) enhances the response of IFN regulatory factors followed by the activation of IFN-sensitive response elements located in the promoter regions of the IFN-α gene and IFN-stimulated genes in the TLR7 reporter THP-1 cells in the presence of R-848, a TLR7 agonist. In ex vivo experiments using human peripheral blood mononuclear cells, LF enhances IFN-α levels in the supernatant in the presence of R-848. Additionally, it increases the expression of IFN-α, human leukocyte antigen (HLA)-DR, and CD86 in pDCs; HLA-DR and CD86 in myeloid dendritic cells; CD69 in CD56 dim natural killer and T killer cells; and IFN-γ in T helper type 1 and B cells in the presence of R-848. The inhibition of phagocytosis or neutralization of nucleolin, a receptor of LF, suppresses LF incorporation into pDCs. These results suggest that pDCs incorporate LF through phagocytosis or nucleolin-mediated endocytosis, and LF enhances TLR7 response in the endosome and subsequent IFN signaling pathway and activates innate and adaptive immune cells. We anticipate that LF modulates antiviral immunity against environmental ssRNA viruses and contributes to homeostasis. Full article
(This article belongs to the Special Issue New Insights into Lactoferrin)
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<p>Interferon (IFN) regulatory factor (IRF) response followed by activation of IFN-sensitive response element (ISRE) in Toll-like receptor 7 reporter THP-1 cells. After 6 h of incubation in the presence or absence of 100 µg/mL lactoferrin (LF) and 10 µg/mL R-848, IRF response followed by ISRE activation was assessed using a luciferase reporter assay. Values are presented as the mean and standard deviation (SD); Open circles represent individual values. n = 3. Different letters above the bars (a, b, c) indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>IFN-α concentration in peripheral blood mononuclear cell (PBMC) culture supernatants. After 24 h incubation of PBMCs with 10 µg/mL R-848 (control) or 10 µg/mL R-848 and 100 µg/mL LF, IFN-α concentration in culture supernatants was measured using an ELISA kit. White bars represent the control group, and gray bars represent the LF-treated group. Values are presented as the mean and SD; Open circles represent individual values. n = 11. * Significantly different from the control group (<span class="html-italic">p</span> &lt; 0.05). LF, lactoferrin.</p>
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<p>Intracellular IFN-α, cell surface human leukocyte antigen (HLA)-DR, and cell surface CD86 expression levels in plasmacytoid dendritic cells (pDCs). After 20–24 h incubation of peripheral blood mononuclear cells with 10 µg/mL R-848 (control) or 10 µg/mL R-848 and 100 µg/mL LF, the expression levels of pDC activity markers, (<b>a</b>) intracellular IFN-α (n = 7), (<b>b</b>) cell surface HLA-DR (n = 4), and (<b>c</b>) cell surface CD86 (n = 4) were measured using flow cytometry. CD123+CD304+ cells are defined as pDCs. White bars represent the control group, and gray bars represent the LF-treated group. Values are presented as the mean and SD. Open circles represent individual values. * Significantly different from the control group (<span class="html-italic">p</span> &lt; 0.05). MFI, geometric mean fluorescence intensity. LF, lactoferrin.</p>
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<p>Expression of activation markers in the immune cells. After 6–24 h incubation of peripheral blood mononuclear cells with 10 µg/mL R-848 (control) or 10 µg/mL R-848 and 100 µg/mL LF, the expression levels of immune cell activity markers were measured using flow cytometry. (<b>a</b>) Cell surface HLA-DR and (<b>b</b>) CD86 in myeloid dendritic cells (mDCs, CD11c+CD123− cells) (n = 4). (<b>c</b>) Intracellular IFN-γ in CD56 bright natural killer (NK) cells (CD3−CD56 bright cells) (n = 3). (<b>d</b>) Cell surface CD69 in CD56 dim NK cells (CD3−CD16+CD56 dim cells) (n = 11). (<b>e</b>) Intracellular IFN-γ in T helper type 1 (Th1) cells (CD3+CD4+CD183+ cells) (n = 7). Cell surface CD69 in (<b>f</b>) T helper cells (CD3+CD4+ cells) (n = 3) and (<b>g</b>) T killer cells (CD3+CD8+ cells) (n = 3). (<b>h</b>) Intracellular IFN-γ in B cells (CD19+ cells) (n = 7). (<b>i</b>) Cell surface CD69 in B cells (CD19+ cells) (n = 3). White bars represent the control group, and gray bars represent the LF-treated group. Values are presented as the mean and SD. Open circles represent individual values. * Significantly different from the control group (<span class="html-italic">p</span> &lt; 0.05). MFI, geometric mean fluorescence intensity; LF, lactoferrin.</p>
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<p>Incorporation of LF into pDCs. (<b>a</b>) Fluorescein isothiocyanate (FITC) fluorescence signals of pDCs, as measured through flow cytometry after 24 h incubation of peripheral blood mononuclear cells with 10 µg/mL R-848 and 100 µg/mL FITC-labeled LF, in the presence or absence of 1 µM cytochalasin D or 5 µg/mL nucleolin-neutralizing antibody. Values are presented as the mean and SD; Open circles represent individual values. n = 3. Different letters above the bars (a, b, c) indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05). (<b>b</b>) Images captured of pDCs, as measured through fluorescence microscopy, after 24 h incubation of isolated pDCs with 10 µg/mL R-848 and 100 µg/mL FITC-labeled LF. Green: FITC-labeled LF; red: pDC membrane surface (CD123); and blue: nuclear staining with Hoechst 33324. Magnification: ×100; scale bar: 10 μm. LF, lactoferrin; pDCs, plasmacytoid dendritic cells.</p>
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17 pages, 4024 KiB  
Article
Anaplastic Lymphoma Kinase (ALK) Inhibitors Enhance Phagocytosis Induced by CD47 Blockade in Sensitive and Resistant ALK-Driven Malignancies
by Federica Malighetti, Matteo Villa, Mario Mauri, Simone Piane, Valentina Crippa, Ilaria Crespiatico, Federica Cocito, Elisa Bossi, Carolina Steidl, Ivan Civettini, Chiara Scollo, Daniele Ramazzotti, Carlo Gambacorti-Passerini, Rocco Piazza, Luca Mologni and Andrea Aroldi
Biomedicines 2024, 12(12), 2819; https://doi.org/10.3390/biomedicines12122819 - 12 Dec 2024
Viewed by 408
Abstract
Background: Anaplastic lymphoma kinase (ALK) plays a role in the development of lymphoma, lung cancer and neuroblastoma. While tyrosine kinase inhibitors (TKIs) have improved treatment outcomes, relapse remains a challenge due to on-target mutations and off-target resistance mechanisms. ALK-positive (ALK+) tumors can evade [...] Read more.
Background: Anaplastic lymphoma kinase (ALK) plays a role in the development of lymphoma, lung cancer and neuroblastoma. While tyrosine kinase inhibitors (TKIs) have improved treatment outcomes, relapse remains a challenge due to on-target mutations and off-target resistance mechanisms. ALK-positive (ALK+) tumors can evade the immune system, partly through tumor-associated macrophages (TAMs) that facilitate immune escape. Cancer cells use “don’t eat me” signals (DEMs), such as CD47, to resist TAMs-mediated phagocytosis. TKIs may upregulate pro-phagocytic stimuli (i.e., calreticulin, CALR), suggesting a potential therapeutic benefit in combining TKIs with an anti-CD47 monoclonal antibody (mAb). However, the impact of this combination on both TKIs-sensitive and resistant ALK+ tumors requires further investigation. Methods: A panel of TKIs-sensitive and resistant ALK+ cancer subtypes was assessed for CALR and CD47 expression over time using flow cytometry. Flow cytometry co-culture and fluorescent microscopy assays were employed to evaluate phagocytosis under various treatment conditions. Results: ALK inhibitors increased CALR expression in both TKIs-sensitive and off-target resistant ALK+ cancer cells. Prolonged TKIs exposure also led to CD47 upregulation. The combination of ALK inhibitors and anti-CD47 mAb significantly enhanced phagocytosis compared to anti-CD47 alone, as confirmed by flow cytometry and fluorescent microscopy. Conclusions: Anti-CD47 mAb can quench DEMs while exposing pro-phagocytic signals, promoting tumor cell phagocytosis. ALK inhibitors induced immunogenic cell damage by upregulating CALR in both sensitive and off-target resistant tumors. Continuous TKIs exposure in off-target resistant settings also resulted in the upregulation of CD47 over time. Combining TKIs with a CD47 blockade may offer therapeutic benefits in ALK+ cancers, especially in overcoming off-target resistance where TKIs alone are less effective. Full article
(This article belongs to the Special Issue Drug Resistance and Novel Targets for Cancer Therapy—Second Edition)
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<p>Surface expression of calreticulin (CALR) after tyrosine kinase inhibitors (TKIs) exposure in both sensitive and resistant ALK–positive cancer subtypes. (<b>A</b>) Representative flow cytometry plot of CALR+/7–AAD- cell population in SUP-M2 cell line, after exposure to crizotinib (0.5 µM) and lorlatinib (0.1 µM) for 20 h, compared to negative control (untreated, UT). (<b>B</b>) Representative histogram bars in terms of percentages of CALR<sup>+</sup>/7–AAD<sup>−</sup> population for each ALK-positive cancer cell lines available in institution, after exposure to different TKIs for 20 h. (<b>C</b>) Expression of CALR in sensitive and lorlatinib–resistant lung adenocarcinoma cell line H3122 after 20 h and 8 days of alectinib and lorlatinib exposure, respectively, (left and middle panels, alectinib 20 h: 2 µM; alectinib 8 days: 200 nM; lorlatinib 20 h and 8 days: 0.1 µM) and CALR expression in resistant neuroblastoma cell line (CLB-Ga-LR1000) after exposure to lorlatinib (1.0 µM) for 20 h and long exposure at day +8 (C, right panel). (<b>D</b>) CALR expression over time in K299 cell line after long exposure to crizotinib (120 nM) compared to the untreated setting (one-way ANOVA with multiple comparisons correction; K299 <span class="html-italic">F</span><sub>(2,6)</sub> = 16.98, SUP-M2 <span class="html-italic">F</span><sub>(2,6)</sub> = 25.36, AS4 <span class="html-italic">F</span><sub>(2,6)</sub> = 18.06, H3122 <span class="html-italic">F</span><sub>(2,6)</sub> = 6.323, H3122-LR100 <span class="html-italic">F</span><sub>(3,8)</sub> = 9.213, CLB-Ga-LR1000 <span class="html-italic">F</span><sub>(2,6)</sub> = 92.04, K299 <sub>CRIZO_120 nM</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 42.55, K299 <sub>CALR+DAY7</sub> <span class="html-italic">F</span><sub>(2,12)</sub> = 26.39, K299 <sub>CALR+DAY9</sub> <span class="html-italic">F</span><sub>(2,12)</sub> = 7.307, K299 <sub>CALR+DAY11</sub> <span class="html-italic">F</span><sub>(1,12)</sub> = 35.79; experimental triplicates; ns: not significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>CD47 surface expression after exposure to TKIs in sensitive and off-target resistant ALK-positive cell lines. (<b>A</b>–<b>F</b>) CD47 surface expression, analyzed using flow cytometry, in lymphoma cell line K299, AS4 cell line, lung cancer cell line H3122 (sensitive to alectinib and resistant to lorlatinib) and neuroblastoma cell line CLB-Ga-LR1000, according to TKIs used; two-way ANOVA with multiple comparisons correction, K299 <span class="html-italic">F</span><sub>(1,32)</sub> = 12.29, AS4<sub>CRIZO</sub> <span class="html-italic">F</span><sub>(1,8)</sub> = 52.08, AS4<sub>LORLA</sub> <span class="html-italic">F</span><sub>(1,8)</sub> = 182.8, H3122 <span class="html-italic">F</span><sub>(1,7)</sub> = 348.5, H3122<sub>LORLA</sub> <span class="html-italic">F</span><sub>(1,8)</sub> = 24.54; experimental triplicates; ns: not significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001; for CLB-Ga-LR1000: unpaired, one-tailed Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Extended analysis of increased phagocytosis in a panel of ALK-positive tumor cell lines, treated with anti-CD47 mAb, previously exposed to ALKi. (<b>A</b>,<b>B</b>) Histogram analysis of phagocytic rate in ALK-positive lymphoma sensitive (K299, SUP-M2) and resistant (AS4) setting, previously exposed to crizotinib or lorlatinib, treated with anti-CD47 mAb (<b>A</b>); crizotinib 0.5 µM, lorlatinib 0.1 µM). (<b>C</b>) Histogram analysis of phagocytic rate in off-target resistant ALK-driven solid cancer cell lines (H3122-LR100, CLB-Ga-LR1000. One-way ANOVA with multiple comparisons correction; K299<sub>CRIZO</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 34.39, K299<sub>LORLA</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 49.50, SUP-M2<sub>CRIZO</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 3.932, SUP-M2<sub>LORLA</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 12.16, AS4<sub>CRIZO</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 8.794, AS4<sub>LORLA</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 9.344, H3122-LR100<sub>LORLA</sub> <span class="html-italic">F</span><sub>(3,8)</sub> = 17.07; CLB-Ga-LR1000<sub>LORLA</sub> <span class="html-italic">F</span><sub>(2,6)</sub> = 102.2; experimental triplicates, <span class="html-italic">n</span> = 3 donors; ns: not significant; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Fluorescent microscopy after incubation of human macrophages with anti-CD47 mAb and the ALK-positive lymphoma AS4 cell line, previously exposed to crizotinib or lorlatinib. (<b>A</b>) Representative images of fluorescent microscopy where Hoechst 33342<sup>+</sup> macrophages were incubated with anti-CD47 mAb and the ALK-positive lymphoma AS4 cell line, labeled with the pH-sensitive dye pHrodo-Red and previously exposed to crizotinib (0.5 µM) or lorlatinib (100 nM) for 20 h. (<b>B</b>) Representative histogram bars of phagocytic index (number of pHrodo-red<sup>+</sup> tumoral cells per 100 macrophages) in case of combination of anti-CD47 mAb and lorlatinib treatment (one-way ANOVA with multiple comparisons correction; AS4 <span class="html-italic">F</span><sub>(5,12)</sub> = 275.6; technical triplicate; <span class="html-italic">n</span> = 1 donor, one experimental cohort; ns: not significant; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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17 pages, 10124 KiB  
Article
KSRP Deficiency Attenuates the Course of Pulmonary Aspergillosis and Is Associated with the Elevated Pathogen-Killing Activity of Innate Myeloid Immune Cells
by Vanessa Bolduan, Kim-Alicia Palzer, Frederic Ries, Nora Busch, Andrea Pautz and Matthias Bros
Cells 2024, 13(24), 2040; https://doi.org/10.3390/cells13242040 - 10 Dec 2024
Viewed by 466
Abstract
The mRNA-binding protein KSRP (KH-type splicing regulatory protein) is known to modulate immune cell functions post-transcriptionally, e.g., by reducing the mRNA stability of cytokines. It is known that KSRP binds the AU-rich motifs (ARE) that are often located in the 3′-untranslated part of [...] Read more.
The mRNA-binding protein KSRP (KH-type splicing regulatory protein) is known to modulate immune cell functions post-transcriptionally, e.g., by reducing the mRNA stability of cytokines. It is known that KSRP binds the AU-rich motifs (ARE) that are often located in the 3′-untranslated part of mRNA species, encoding dynamically regulated proteins as, for example, cytokines. Innate myeloid immune cells, such as polymorphonuclear neutrophils (PMNs) and macrophages (MACs), eliminate pathogens by multiple mechanisms, including phagocytosis and the secretion of chemo- and cytokines. Here, we investigated the role of KSRP in the phenotype and functions of both innate immune cell types in the mouse model of invasive pulmonary aspergillosis (IPA). Here, KSRP−/− mice showed lower levels of Aspergillus fumigatus conidia (AFC) and an increase in the frequencies of PMNs and MACs in the lungs. Our results showed that PMNs and MACs from KSRP−/− mice exhibited an enhanced phagocytic uptake of AFC, accompanied by increased ROS production in PMNs upon stimulation. A comparison of RNA sequencing data revealed that 64 genes related to inflammatory and immune responses were shared between PMNs and MACs. The majority of genes upregulated in PMNs were involved in metabolic processes, cell cycles, and DNA repair. Similarly, KSRP-deficient PMNs displayed reduced levels of apoptosis. In conclusion, our results indicate that KSRP serves as a critical negative regulator of PMN and MAC anti-pathogen activity. Full article
(This article belongs to the Special Issue Innate Immunity in Health and Disease)
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<p>KSRP deficiency led to the higher production of proinflammatory mediators following LPS or AFC stimulation. Here, 10<sup>6</sup> spleen cells were stimulated with 1 µg/mL of LPS or 1 × 10<sup>6</sup> AFC for 16 h. Supernatants were collected, and the cytokine content was determined. Data denote the mean ± SEM of <span class="html-italic">n</span> = 4–7 analyses (* <span class="html-italic">p</span> &lt; 0.05; two-tailed Student’s <span class="html-italic">t</span>-test).</p>
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<p>Depletion of PMNs and the inactivation of the KSRP gene led to resistance against <span class="html-italic">A. fumigatus</span> infection. (<b>A</b>) One day prior to fungal infection, the control mice were i.p. injected with an α-Ly6G antibody to achieve PMN depletion (green). 24 h after inoculation, the first group (blue) was sacrificed (†), whereas the second group (orange) was analyzed 14 days following inoculation with AFC (created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). (<b>B</b>) The clinical course of IPA was monitored for 2 weeks. (<b>C</b>) <span class="html-italic">TEF1</span> mRNA expression, as a marker for AFC [<a href="#B49-cells-13-02040" class="html-bibr">49</a>], was assessed by qRT-PCR and normalized to <span class="html-italic">GAPDH</span> mRNA expression. The lungs of KSRP<sup>−/−</sup> mice contained less AFC-specific mRNA compared to WT mice 1 day post-inoculation. (<b>D</b>) Flow cytometric analysis showed higher frequencies of PMNs in KSRP-deficient mice, whereas MACs, EOSs, and DCs showed no genotype-dependent differences in the BALF. (<b>E</b>) Flow cytometric analyses of lung tissue displayed higher frequencies of PMNs and MACs, whereas EOSs and DCs showed no genotype-dependent differences 1 day after inoculation. Data denote the mean ± SEM of three samples/group. Statistically significant differences are indicated (two-tailed Student’s <span class="html-italic">t</span>-test).</p>
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<p>KSRP-deficient PMNs are characterized by the enhanced expression of genes involved in the inflammatory response upon stimulation. (<b>A</b>,<b>D</b>) TNF-α- and IL-6-related genes (red) were further analyzed for gene clusters using the STRING database. (<b>B</b>,<b>E</b>) The mRNA expression of TNF-α and IL-6 in PMNs stimulated with 1 µg/mL of LPS for different time periods (3 h and 6 h). Data denote the mean ± SEM of <span class="html-italic">n</span> = 9 (** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05; versus untreated WT cells; two-tailed Student’s <span class="html-italic">t</span>-test). (<b>C</b>,<b>F</b>) The protein expression of TNF-α and IL-6 in the supernatants of LPS-stimulated PMNs after different time periods (3 h and 16 h). Data denote the mean ± SEM of <span class="html-italic">n</span> = 6–9 samples/group (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; two-tailed Student’s <span class="html-italic">t</span>-test).</p>
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<p>Upregulation of genes involved in pathogen defense in KSRP-deficient PMNs and MACs following LPS stimulation. (<b>A</b>) Using a Venn diagram calculator (<a href="https://bioinformatics.psb.ugent.be/webtools/Venn/" target="_blank">https://bioinformatics.psb.ugent.be/webtools/Venn/</a> (accessed on 14 September 2024)), we calculated the same and different upregulated genes after LPS stimulation within PMNs and MACs. (<b>B</b>) The STRING database revealed that 64 equally upregulated genes in PMNs and BMDMs were interlinked and contributed to a positive defense regulation against pathogens and the activation of innate immunity.</p>
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<p>KSRP deficiency enhances PMN and MAC phagocytosis. PMNs were immunomagnetically isolated via Ly6G from the bone marrow of WT and KSRP<sup>−/−</sup> mice, whereas BMDMs were differentiated from bone marrow with M-CSF for 7 days. (<b>A</b>,<b>B</b>) 1 × 10<sup>5</sup> PMNs or BMDMs were cultured with serum-preincubated GFP-expressing AFC at 4 °C or 37 °C with the indicated ratios. This was performed both at 4 °C and 37 °C to distinguish between AFC adhesion and uptake, respectively. After 3 h, the frequencies of GFP-positive PMNs and MACs were assessed by flow cytometric analysis. Data denote the mean ± SEM of six samples/group (** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05; two-tailed Student’s <span class="html-italic">t</span>-test). Exemplary primary data showing the pronounced uptake of GFP-expressing AFC by KSRP-deficient PMNs (<b>C</b>) and MACs (<b>D</b>). The gating strategy is illustrated in <a href="#app1-cells-13-02040" class="html-app">Figure S7</a>.</p>
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<p>KSRP deficiency enhances ROS production by PMNs. Here, 10<sup>5</sup> PMNs were stained with DCFDA, treated with 100 ng/mL of GM-CSF plus the indicated stimuli and 1 µg/mL of LPS (<b>A</b>) or 1 × 10<sup>5</sup> AFC (<b>B</b>), and measured in 15 min intervals for 3 h. Data denote the mean ± SEM of nine samples/group (** <span class="html-italic">p</span> &lt; 0.01; two-tailed Student’s <span class="html-italic">t</span>-test).</p>
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<p>Stimulation with LPS leads to reduced apoptosis in the PMNs of KSRP<sup>−/−</sup> mice. PMNs were immunomagnetically isolated via Ly6G from bone marrow. Here, 10<sup>6</sup> PMNs were cultured without or with 1 µg/mL of LPS for 6 h. Afterwards, surface receptors were stained for flow cytometry analysis. (<b>A</b>) Stimulation leads to the increased frequency of PMNs in KSRP<sup>−/−</sup> mice. Data denote the mean ± SEM of nine samples/group (** <span class="html-italic">p</span> &lt; 0.01; two-tailed Student’s <span class="html-italic">t</span>-test). (<b>B</b>) An assessment of PMN apoptosis revealed a lower expression of the apoptosis marker Annexin V in stimulated KSRP-deficient PMNs. Shown are the mean ± SEM of <span class="html-italic">n</span> = 6 analyses (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001; two-tailed Student’s <span class="html-italic">t</span>-test). (<b>C</b>) Exemplary flow cytometry data showing attenuated apoptosis by PMNs from KSRP<sup>−/−</sup> mice. The complete gating strategy is illustrated in <a href="#app1-cells-13-02040" class="html-app">Figure S9</a>.</p>
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16 pages, 2939 KiB  
Article
Extraction Methods and Characterization of β-Glucans from Yeast Lees of Wines Produced Using Different Technologies
by Ana Chioru, Aurica Chirsanova, Adriana Dabija, Ionuț Avrămia, Alina Boiştean and Ancuța Chetrariu
Foods 2024, 13(24), 3982; https://doi.org/10.3390/foods13243982 - 10 Dec 2024
Viewed by 728
Abstract
Wine lees, the second most significant by-product of winemaking after grape pomace, have received relatively little attention regarding their potential for valorization. Despite their rich content in bioactive components such as β-glucans, industrial utilization faces challenges, particularly due to variability in their composition. [...] Read more.
Wine lees, the second most significant by-product of winemaking after grape pomace, have received relatively little attention regarding their potential for valorization. Despite their rich content in bioactive components such as β-glucans, industrial utilization faces challenges, particularly due to variability in their composition. This inconsistency impacts the reliability and standardization of final products, limiting broader adoption in industrial applications. β-Glucans are dietary fibers or polysaccharides renowned for their diverse bioactive properties, including immunomodulatory, antioxidant, anti-inflammatory, antitumor, and cholesterol- and glucose-lowering effects. They modulate the immune system by activating Dectin-1 and TLR receptors on immune cells, enhancing phagocytosis, cytokine production, and adaptive immune responses. Their antioxidant activity arises from neutralizing free radicals and reducing oxidative stress, thereby protecting cells and tissues. β-Glucans also exhibit antitumor effects by inhibiting cancer cell growth, inducing apoptosis, and preventing angiogenesis, the formation of new blood vessels essential for tumor development. Additionally, they lower cholesterol and glucose levels by forming a viscous gel in the intestine, which reduces lipid and carbohydrate absorption, improving metabolic health. The biological activity of β-glucans varies with their molecular weight and source, further highlighting their versatility and functional potential. This study investigates how grape variety, vinification technology and extraction methods affect the yield and properties of β-glucans extracted from wine lees. The physico-chemical and mineral composition of different wine lees were analyzed, and two extraction methods of β-glucans from wine lees were tested: acid-base extraction and autolysis. These two methods were also tested under ultrasound-assisted conditions at different frequencies, as well as without the use of ultrasound. The β-glucan yield and properties were evaluated under different conditions. FTIR spectroscopy was used to assess the functional groups and structural characteristics of the β-glucans extracted from the wine lees, helping to confirm their composition and quality. Rheological behavior of the extracted β-glucans was also assessed to understand the impact of extraction method and raw material origin. The findings highlight that vinification technology significantly affects the composition of wine lees, while both the extraction method and yeast origin influence the yield and type of β-glucans obtained. The autolysis method provided higher β-glucan yields (18.95 ± 0.49% to 39.36 ± 0.19%) compared to the acid–base method (3.47 ± 0.66% to 19.76 ± 0.58%). FTIR spectroscopy revealed that the β-glucan extracts contain a variety of glucan and polysaccharide types, with distinct β-glucans (β-1,4, β-1,3, and β-1,6) identified through specific absorption peaks. The rheological behavior of suspensions exhibited pseudoplastic or shear-thinning behavior, where viscosity decreased significantly as shear rate increased. This behavior, observed across all β-glucan extracts, is typical of polymer-containing suspensions. These insights are critical for optimizing β-glucan extraction processes, supporting sustainability efforts and waste valorization in the wine industry. Efficient extraction of β-glucans from natural sources like wine lees offers a promising path toward their industrial application as valuable functional compounds. Full article
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<p>Microscopic images of winery yeast lees (100× magnification objective). (<b>a</b>) Semi-dry white wine (SVAM), (<b>b</b>) sweet white wine (SVR), (<b>c</b>) dry red wine (SVRS), (<b>d</b>) sparkling white wine (SVS).</p>
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<p>FT−IR−ATR spectra of the most representative samples.</p>
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<p>Shear rate dependence of viscosity for 2% suspension of β-glucans extracted by the acid–base method.</p>
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<p>Shear rate dependence of viscosity for 2% suspension of β-glucans extracted by autolysis.</p>
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12 pages, 4669 KiB  
Article
Metagenomic Insight into the Associated Microbiome in Plasmodia of Myxomycetes
by Xueyan Peng, Shu Li, Wenjun Dou, Mingxin Li, Andrey A. Gontcharov, Zhanwu Peng, Bao Qi, Qi Wang and Yu Li
Microorganisms 2024, 12(12), 2540; https://doi.org/10.3390/microorganisms12122540 - 10 Dec 2024
Viewed by 495
Abstract
During the trophic period of myxomycetes, the plasmodia of myxomycetes can perform crawling feeding and phagocytosis of bacteria, fungi, and organic matter. Culture-based studies have suggested that plasmodia are associated with one or several species of bacteria; however, by amplicon sequencing, it was [...] Read more.
During the trophic period of myxomycetes, the plasmodia of myxomycetes can perform crawling feeding and phagocytosis of bacteria, fungi, and organic matter. Culture-based studies have suggested that plasmodia are associated with one or several species of bacteria; however, by amplicon sequencing, it was shown that up to 31–52 bacteria species could be detected in one myxomycete, suggesting that the bacterial diversity associated with myxomycetes was likely to be underestimated. To fill this gap and characterize myxomycetes’ microbiota and functional traits, the diversity and functional characteristics of microbiota associated with the plasmodia of six myxomycetes species were investigated by metagenomic sequencing. The results indicate that the plasmodia harbored diverse microbial communities, including eukaryotes, viruses, archaea, and the dominant bacteria. The associated microbiomes represented more than 22.27% of the plasmodia genome, suggesting that these microbes may not merely be parasitic or present as food but rather may play functional roles within the plasmodium. The six myxomycetes contained similar bacteria, but the bacteria community compositions in each myxomycete were species-specific. Functional analysis revealed a highly conserved microbial functional profile across the six plasmodia, suggesting they may serve a specific function for the myxomycetes. While the host-specific selection may shape the microbial community compositions within plasmodia, functional redundancy ensures functional stability across different myxomycetes. Full article
(This article belongs to the Section Microbiomes)
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<p>Six plasmodia of myxomycetes cultured on water agar media: phaneroplasmodium (<b>a</b>) <span class="html-italic">D. squamulosum</span>, (<b>b</b>) <span class="html-italic">D. nigripes</span>, (<b>c</b>) <span class="html-italic">F. gyrosa</span>, (<b>d</b>) <span class="html-italic">B. melanospora</span>, and aphanoplasmodium (<b>e</b>) <span class="html-italic">A. cinerea</span>, (<b>f</b>) <span class="html-italic">M. scintillans</span>.</p>
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<p>Relative abundance of microbial communities in each plasmodium. The domain level (<b>a</b>) and top 15 phylum level (<b>b</b>) are displayed at community compositions. Phylum outside the top 15 samples was assigned as “Others”.</p>
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<p>Bacterial community composition and diversity analysis of each plasmodium. (<b>a</b>) Hierarchical clustering analysis (weighted Unifrac UPGMA) and relative abundance of bacterial communities associated with each plasmodium and Venn diagrams show the number and abundance of shared and unique bacteria in each plasmodium at the genus level. (<b>b</b>) The α diversity (Chao, Shannon index, and Pielou_e) and (<b>c</b>) PCoA analysis using the Bray–Curtis distance metric showed the plasmodia bacterial communities’ diversity. (<b>d</b>) Relative abundances of the top 10 phylum levels of bacterial communities. (<b>e</b>) Venn diagram shows the shared and unique bacteria at the species level in each plasmodia sample. The differences were considered significant when <span class="html-italic">p</span> values &lt; 0.05. *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Relative abundance of Gram-positive/Gram-negative bacteria in each plasmodium.</p>
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<p>Functional analysis of plasmodia-associated bacteria. (<b>a</b>) A comparison of the top 25 COG functional categories in the six plasmodia. (<b>b</b>) Gene count and relative abundance of CAZy class categories. (<b>c</b>) Functional KEGG level 1 and (<b>d</b>) KEGG level 2 pathway descriptions, and (<b>e</b>) relative abundance of top 20 KEGG level 3 pathway categories.</p>
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<p>Composition and functional analysis of probiotics in six myxomycetes. Relative abundance of probiotics at genus level (<b>a</b>) and top 25 of functional composition of probiotics (<b>b</b>) was exhibited in each plasmodium.</p>
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18 pages, 18524 KiB  
Article
A Graphene-Based Bioactive Product with a Non-Immunological Impact on Mononuclear Cell Populations from Healthy Volunteers
by María del Prado Lavín-López, Mónica Torres-Torresano, Eva María García-Cuesta, Blanca Soler-Palacios, Mercedes Griera, Martín Martínez-Rovira, José Antonio Martínez-Rovira, Diego Rodríguez-Puyol and Sergio de Frutos
Nanomaterials 2024, 14(23), 1945; https://doi.org/10.3390/nano14231945 - 4 Dec 2024
Viewed by 482
Abstract
We previously described GMC, a graphene-based nanomaterial obtained from carbon nanofibers (CNFs), to be biologically compatible and functional for therapeutic purposes. GMC can reduce triglycerides’ content in vitro and in vivo and has other potential bio-functional effects on systemic cells and the potential [...] Read more.
We previously described GMC, a graphene-based nanomaterial obtained from carbon nanofibers (CNFs), to be biologically compatible and functional for therapeutic purposes. GMC can reduce triglycerides’ content in vitro and in vivo and has other potential bio-functional effects on systemic cells and the potential utility to be used in living systems. Here, immunoreactivity was evaluated by adding GMC in suspension at the biologically functional concentrations, ranging from 10 to 60 µg/mL, for one or several days, to cultured lymphocytes (T, B, NK), either in basal or under stimulating conditions, and monocytes that were derived under culture conditions to pro-inflammatory (GM-MØ) or anti-inflammatory (M-MØ) macrophages. All stirpes were obtained from human peripheral mononuclear cells (PBMCs) from anonymized healthy donors. The viability (necrosis, apoptosis) and immunological activity of each progeny was analyzed using either flow cytometry and/or other analytical determinations. A concentration of 10 to 60 µg/mL GMC did not affect lymphocytes’ viability, either in basal or active conditions, during one or more days of treatment. The viability and expression of the inflammatory interleukin IL-1β in the monocyte cell line THP-1 were not affected. Treatments with 10 or 20 µg/mL GMC on GM-MØ or M-MØ during or after their differentiation process promoted phagocytosis, but their viability and the release of the inflammatory marker activin A by GM-MØ were not affected. A concentration of 60 µg/mL GMC slightly increased macrophages’ death and activity in some culture conditions. The present work demonstrates that GMC is safe or has minimal immunological activity when used in suspension at low concentrations for pre-clinical or clinical settings. Its biocompatibility will depend on the dose, formulation or way of administration and opens up the possibility to consider GMC or other CNF-based biomaterials for innovative therapeutic strategies. Full article
(This article belongs to the Special Issue A Sustainable Future Using 2D and 1D Nanomaterials and Nanotechnology)
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<p>GMC characterization: Representative images of agglomerated non-suspended dry GMC obtained through (<b>A</b>) High-Resolution Transmission Electron Microscopy (TEM, scale bar 200 nm) and (<b>B</b>,<b>C</b>) Scanning Electron Microscopy (SEM, scale bars 100 and 50 nm, respectively). (<b>D</b>) Raman analysis indicating D, D3, D4 and G bands, (<b>E</b>) high-resolution spectrum of C1s obtained using X-ray photoelectron spectroscopy (XPS).</p>
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<p>Immunological activity of different lymphocyte populations during GMC treatment. PBMCs were isolated from buffy coats of healthy donors. In some cases, when required, specific cell lineages were purified. After appropriate treatments for activation and/or GMC stimulation, cells were stained with specific surface antibodies for the active lineages and analyzed using flow cytometry. (<b>A</b>) T-lymphocytes in the context of total PBMCs were stimulated or not under culture conditions with 20 ng/mL IL2 and 0.5 μg/m PHA-M for 96 h and 20 ng/mL IL2 for another 48 h. Afterwards, cells were treated with GMC (0, 20 or 60 µg/mL) for another 24 h. Histograms represent the % of T-lymphocytes (CD3+) positive for the activation makers CD69 and CD25. (<b>B</b>) B-lymphocytes were isolated using a commercial kit and co-treated for 24 h with GMC (0, 20 or 60 µg/mL) and stimulated or not (unstimulated) with B-lymphocyte activators (1μM ODN 2395-CpG, 1 mg/mL IgG + IgM and 100 U/mL IL4). Histograms represent the % of B-lymphocytes (CD19+) positive for the activation markers CD23, CD69 and CD86. (<b>C</b>) PBMCs were cultured for 7 days with GMC (0, 20 or 60 µg/mL) and co-cultured afterwards for 2 h with NK-degranulating target cell type K562 or no target (unstimulated). Histograms represent the % of degranulating NK cells (CD3−, CD56+, LAMP1+). The results are presented as means ± SEM from <span class="html-italic">n</span> = 6 independent donors. No statistically significant differences were found between control and GMC treatments within any of the studied subpopulations. (n.s.) = no significant differences vs. control.</p>
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<p>Viability and immunological activity of cultured human monocyte THP-1 cell line. THP-1 was treated with GMC (0, 20 µg/mL) for 24 h. (<b>A</b>) Viability was determined using trypan blue cellular exclusion. (<b>B</b>) IL-1β mRNA expression normalized to β-actin levels, determined using RT-qPCR. TGF-β1 (20 ng/mL) was added as an inflammatory positive control. Data are expressed as mean ± SEM from <span class="html-italic">n</span> = 12 experiments. * = <span class="html-italic">p</span> &lt; 0.05 vs. control.</p>
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<p>Viability and activin A production of GM-MØ during GMC treatment. (<b>A</b>) Ratios vs. the control of necrotic (Annexin V− and PI+) and (<b>B</b>) apoptotic (Annexin V+, PI−) cells and (<b>C</b>) activin A release in the supernatant (pro-inflammatory marker) from fully differentiated GM-MØ after 24 h of GMC treatments (10, 20 or 60 µg/mL) or when left untreated (control). (<b>D</b>) Ratios of necrotic, (<b>E</b>) apoptotic and (<b>F</b>) activin A levels released from GM-MØ after 7 days of GMC treatment during their macrophage differentiation. Data are expressed as mean ± SEM from <span class="html-italic">n</span> = 3–6 experiments. * = <span class="html-italic">p</span> &lt; 0.05 vs. control.</p>
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<p>Morphological pattern and phagocytosis during GMC treatment of fully differentiated GM-MØ. (<b>A</b>) Representative flow cytometry analysis box plot for cell size (FSC) and complexity (SSC) comparison of GM-MØ after 24 h of 20 µg/mL GMC treatment or when left untreated (control). (<b>B</b>) Representative contrast phase microscope pictures of the cells after GMC treatment. Scale bars: 100 µm.</p>
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<p>Morphological pattern and phagocytosis during GMC treatment during monocyte-to-GM-MØ differentiation. (<b>A</b>) Representative flow cytometry analysis box plot for cell size (FSC) and complexity (SSC) comparison of GM-MØ after 7 days of 10 µg/mL GMC treatment or when left untreated (control). (<b>B</b>) Representative contrast phase microscope pictures of the cells after GMC treatment. Scale bars: 100 µm.</p>
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<p>Viability of M-MØ during GMC treatment. (<b>A</b>) Ratios vs. control of necrotic (Annexin V− and PI+) and (<b>B</b>) apoptotic (Annexin V+, PI−) fully differentiated M-MØ after 24 h of GMC treatments (10, 20 or 60 µg/mL) or when left untreated (control). (<b>C</b>) Ratios of necrotic and (<b>D</b>) apoptotic M-MØ after 7 days of GMC treatment during their macrophage differentiation. Data are expressed as mean ± SEM from <span class="html-italic">n</span> = 3 experiments. * = <span class="html-italic">p</span> &lt; 0.05 vs. control.</p>
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<p>Morphological pattern and phagocytosis during GMC treatment of fully differentiated M-MØ. (<b>A</b>) Representative flow cytometry analysis box plot for cell size (FSC) and complexity (SSC) comparison of M-MØ after 24 h of 20 µg/mL GMC treatment or when left untreated (control). (<b>B</b>) Representative contrast phase microscope pictures of the cells after GMC treatment. Scale bars: 100 µm.</p>
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<p>Morphological pattern and phagocytosis during GMC treatment during M-MØ differentiation. (<b>A</b>) Representative flow cytometry analysis box plot for cell size (FSC) and complexity (SSC) comparison of M-MØ after 7 days of 10 µg/mL GMC treatments or when left untreated (control). (<b>B</b>) Representative contrast phase microscope pictures of the cells after GMC treatment. Scale bars: 100 µm.</p>
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23 pages, 4536 KiB  
Article
Proteomic Profile Regulated by the Immunomodulatory Jusvinza Drug in Neutrophils Isolated from Rheumatoid Arthritis Patients
by Mabel Hernández-Cedeño, Arielis Rodríguez-Ulloa, Yassel Ramos, Luis J. González, Anabel Serrano-Díaz, Katharina Zettl, Jacek R. Wiśniewski, Gillian Martinez-Donato, Gerardo Guillen-Nieto, Vladimir Besada and María del Carmen Domínguez-Horta
Biomedicines 2024, 12(12), 2740; https://doi.org/10.3390/biomedicines12122740 - 29 Nov 2024
Viewed by 800
Abstract
Jusvinza is an immunomodulatory drug composed of an altered peptide ligand (APL) designed from a novel CD4+ T cell epitope of human heat shock protein 60 (HSP60), an autoantigen involved in the pathogenesis of rheumatoid arthritis (RA). The peptide induces regulatory T cells [...] Read more.
Jusvinza is an immunomodulatory drug composed of an altered peptide ligand (APL) designed from a novel CD4+ T cell epitope of human heat shock protein 60 (HSP60), an autoantigen involved in the pathogenesis of rheumatoid arthritis (RA). The peptide induces regulatory T cells and decreases levels of TNF-α and IL-17; pre-clinical and phase I clinical studies support its use for the treatment of RA. This peptide was repositioned for the treatment of COVID-19 patients with signs of hyperinflammation. Neutrophils play a pathogenic role in both RA and severe forms of COVID-19. To add novel evidence about the mechanism of action of Jusvinza, the proteomic profile regulated by this peptide of neutrophils isolated from four RA patients was investigated using LC-MS/MS and bioinformatics analysis. A total of 149 proteins were found to be differentially modulated in neutrophils treated with Jusvinza. The proteomic profile regulated by Jusvinza is characterized by the presence of proteins related to RNA splicing, phagocytosis, endocytosis, and immune functions. In response to Jusvinza treatment, several proteins that regulate the NF-κB signaling pathway were differentially modulated, supporting the peptide’s anti-inflammatory effect. Proteins related to metabolic pathways that supply ATP for cellular functions or lipid metabolites with immunoregulatory properties were also identified. Additionally, several structural components of neutrophil extracellular traps (NETs) were decreased in Jusvinza-treated cells, supporting its impairment of this biological process. Of note, these findings were validated by in vitro experiments which confirmed that Jusvinza decreased NET formation. Such results provide evidence of the molecular mechanism of action and support the therapeutic potentialities of Jusvinza to treat other diseases characterized by hyperinflammation besides RA and COVID-19. Full article
(This article belongs to the Special Issue Neutrophils in Immunity and Diseases)
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Graphical abstract

Graphical abstract
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<p>Proteomic profile of neutrophils treated with Jusvinza. (<b>A</b>) Workflow for the exploration and analysis of the proteomic profile modulated in response to Jusvinza treatment. (<b>B</b>) Number of significantly modulated proteins in Jusvinza-treated neutrophils at 6 h and 18 h. Down- and up-regulated proteins are highlighted in blue and red, respectively. The table shows the overlapping proteins between the two datasets. (*) MED-FASP: multienzyme digestion filter-assisted sample preparation [<a href="#B33-biomedicines-12-02740" class="html-bibr">33</a>].</p>
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<p>Enrichment analysis of differentially modulated proteins in neutrophils treated with Jusvinza at 6 h and 18 h. Biological processes and pathways significantly represented in the proteomic profiles (<span class="html-italic">p</span>-value &lt; 0.01, enrichment factor &gt; 1.5) were identified using the Metascape gene annotation and analysis resource (<a href="https://metascape.org/" target="_blank">https://metascape.org/</a>, accessed on 5 November 2020). In the heatmap and bar graph, enriched terms are colored according to <span class="html-italic">p</span>-values.</p>
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<p>Functional association networks between differentially modulated proteins and some (<b>A</b>) biological processes and (<b>B</b>) subcellular locations which were found to be over-represented in the proteomic profile. In networks, proteins are shown as yellow circles, with the outside circle representing the expression level (blue, decreased; red, increased; white, not differentially modulated) and colored in a clockwise fashion according to the fold change at each time point (6 h and 18 h). Proteins that were modulated at both time points are highlighted in green.</p>
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<p>Protein–protein interaction networks associated with the proteomic profile modulated at 6 h (<b>A</b>) and 18 h (<b>B</b>) in Jusvinza-treated neutrophils. In both networks, proteins are represented according to the expression level (blue, decreased; red, increased; yellow, not identified); dark and light colors represent proteins identified in neutrophils isolated from four and three AR patients, respectively. Biological processes and proteins complexes gathered using the STRING functional enrichment tool and datamining are indicated by squares and green colors, respectively. Proteins related to the immune system are highlighted by a bold circle. Direct interactions between differentially modulated proteins are represented by black edges.</p>
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<p>(<b>A</b>) Representative fluorescence images of LPS-induced NETosis. Neutrophils isolated from healthy donors were subjected to different experimental conditions: (<b>a</b>) unstimulated neutrophils, (<b>b</b>) LPS-stimulated neutrophils, (<b>c</b>) neutrophils stimulated with 5 μg of Jusvinza, (<b>d</b>) neutrophils simultaneously stimulated with LPS and 5 μg of Jusvinza, (<b>e</b>) neutrophils treated with 5 μg of Jusvinza 30 min after pre-stimulation with LPS. The DNA was stained with 10 μg/mL propidium iodide solution. Images were captured at 20× magnification. White arrows indicate neutrophils in NETosis. (<b>B</b>) Quantification of NETs induced by LPS in neutrophils treated with Jusvinza. NETs were quantified using neutrophils isolated from three healthy donors. Five fluorescent images were acquired for each experimental condition. Neutrophils in NETosis were defined when the nucleus area was ≥1-fold the cellular area. Results are expressed as a fraction of the number of neutrophils in NETosis/the total number of neutrophils. Significant differences were calculated by one-way ANOVA followed by Tukey’s multiple comparisons test (* <span class="html-italic">p</span>-value &lt; 0.05, *** <span class="html-italic">p</span>-value &lt; 0.0002, **** <span class="html-italic">p</span>-value &lt; 0.0001).</p>
Full article ">Figure 5 Cont.
<p>(<b>A</b>) Representative fluorescence images of LPS-induced NETosis. Neutrophils isolated from healthy donors were subjected to different experimental conditions: (<b>a</b>) unstimulated neutrophils, (<b>b</b>) LPS-stimulated neutrophils, (<b>c</b>) neutrophils stimulated with 5 μg of Jusvinza, (<b>d</b>) neutrophils simultaneously stimulated with LPS and 5 μg of Jusvinza, (<b>e</b>) neutrophils treated with 5 μg of Jusvinza 30 min after pre-stimulation with LPS. The DNA was stained with 10 μg/mL propidium iodide solution. Images were captured at 20× magnification. White arrows indicate neutrophils in NETosis. (<b>B</b>) Quantification of NETs induced by LPS in neutrophils treated with Jusvinza. NETs were quantified using neutrophils isolated from three healthy donors. Five fluorescent images were acquired for each experimental condition. Neutrophils in NETosis were defined when the nucleus area was ≥1-fold the cellular area. Results are expressed as a fraction of the number of neutrophils in NETosis/the total number of neutrophils. Significant differences were calculated by one-way ANOVA followed by Tukey’s multiple comparisons test (* <span class="html-italic">p</span>-value &lt; 0.05, *** <span class="html-italic">p</span>-value &lt; 0.0002, **** <span class="html-italic">p</span>-value &lt; 0.0001).</p>
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<p>Molecular basis of Jusvinza’s mechanism of action on neutrophils. Jusvinza treatment inhibits pro-inflammatory cytokine release and NETosis. Supporting Jusvinza’s anti-inflammatory effect, the proteomic profile includes several transcription factors (YBX1 and FLI1) and regulator proteins of the heat shock response (HSBP1) and the NF κB signaling pathway. The abundance levels of positive regulators of NF-κB signaling (LIGHT, MTDH, HMGB1, CDC37, and LRRFIP2) were decreased, while negative regulators (APPL2, IKBIP, and ASCC1) were increased in Jusvinza-treated neutrophils. Structural components of NETs (histones and HMGB1) were also decreased in response to Jusvinza treatment. Furthermore, the peptide could inhibit PAD4 activation dependent on Ca<sup>2+</sup>/ROS and consequently suppress NET release. Additionally, proteins related to lipid metabolic pathways (DBI, HELZ2, MBOAT7, and PLIN3) were identified, some of which regulate the synthesis of lipid metabolites (LTB4 and PGE2) with immunoregulatory properties. Juzvinza treatment also modulates proteins related to neutrophil effector functions, such as phagocytosis/endocytosis (CLTA), migration, and priming of neutrophils (HCLS1, WIP, and WASP), probably decreasing the over-activation of such cells in chronic inflammatory conditions. Proteins are represented as boxes and colored according to their expression levels (blue, decreased; red, increased; grey, not identified). In signaling pathways, the lines indicate regulatory events (arrows: activation, lines: inhibition).</p>
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19 pages, 3838 KiB  
Article
Suppression of Metastasis of Colon Cancer to Liver in Mouse Models by Pretreatment with Extracellular Vesicles Derived from Nanog-Overexpressing Colon-26 Cancer Cells
by Takuya Henmi, Hideaki Matsuoka, Noa Katayama and Mikako Saito
Int. J. Mol. Sci. 2024, 25(23), 12794; https://doi.org/10.3390/ijms252312794 - 28 Nov 2024
Viewed by 402
Abstract
It has been demonstrated that cancer cells that have survived cancer treatment may be more malignant than the original cancer cells. These cells are considered the main cause of metastasis in prognosis. A Nanog-overexpressing colon-26 (Nanog+colon26) was generated to [...] Read more.
It has been demonstrated that cancer cells that have survived cancer treatment may be more malignant than the original cancer cells. These cells are considered the main cause of metastasis in prognosis. A Nanog-overexpressing colon-26 (Nanog+colon26) was generated to obtain such a malignant cancer cell model, which was confirmed by enhancement of metastatic potential by in vivo tests using mice. Extracellular vesicles (EVs) secreted from Nanog+colon26 cells (Nanog+colon26EVs) were administered to mice three times per week for three weeks. Subsequently, Nanog+colon26 cells were administered, and metastatic colonies were analyzed two weeks later. The results demonstrated that the administration of EVs suppressed metastasis. Nanog+colon26EVs enhanced phagocytic activity and M1 marker CD80 of a macrophage cell line J774.1. These suggested the enforcement of tumor-suppressive properties of macrophages and their contribution to the in vivo suppression of metastasis. Small RNA sequencing was conducted to identify Nanog-dependent miRNAs that exhibited significant changes (Fc ≥ 1.5 or Fc ≤ 1/1.5; p < 0.05) in Nanog+colon26EVs relative to colon26EVs. Nine miRNAs (up-regulated: four, down-regulated: five) were identified, and 623 genes were predicted to be their target genes. Of the 623 genes identified, nine genes were predicted to be highly relevant to macrophage functions such as phagocytosis. Full article
(This article belongs to the Special Issue The Molecular Basis of Extracellular Vesicles in Health and Diseases)
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Figure 1

Figure 1
<p>Properties of <span class="html-italic">Nanog</span>⁺colon26 cells. (<b>A</b>) Relative expression of mRNA of <span class="html-italic">Nanog</span>; mean ± SD for <span class="html-italic">n</span> = 3. (<b>B</b>) Proliferation activity; mean ± SD for <span class="html-italic">n</span> = 3. (<b>C</b>) Migration activity; mean ± SD for <span class="html-italic">n</span> = 3. (<b>D</b>) Invasion activity; mean ± SD for <span class="html-italic">n</span> = 3. (<b>E</b>) Matrix metalloproteinase activity; mean ± SD for <span class="html-italic">n</span> = 3. **: <span class="html-italic">p</span> &lt; 0.01; *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Metastatic properties of <span class="html-italic">Nanog</span>⁺colon26 cells. (<b>A</b>) Colon cancer colonies metastasize to the liver. Colony: circled by yellow lines. (<b>B</b>) Quantification of metastasis by adding up the area of colonies by using ImageJ. (<b>C</b>) Colony area per mouse. colon-26: <span class="html-italic">n</span> = 7; <span class="html-italic">Nanog</span><sup>+</sup>colon26: <span class="html-italic">n</span> = 6; *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Western blotting performed to confirm the presence of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs markers: (<b>A</b>) CD81, (<b>B</b>) TSG101, (<b>C</b>) Alix, and the absence of a negative marker, (<b>D</b>) GM130. Gapdh: loading control.</p>
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<p>Western blotting performed to confirm the presence of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs markers: (<b>A</b>) CD81, (<b>B</b>) TSG101, (<b>C</b>) Alix, and the absence of a negative marker, (<b>D</b>) GM130. Gapdh: loading control.</p>
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<p>Distribution of EVs in the body. (<b>A</b>) Fluorescent images of NIR815-labeled <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs accumulated in each organ. (<b>B</b>) Accumulation of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs (<span style="color:#9CC2E5">■</span>) and colon26EVs (<span style="color:#FF7C80">■</span>) in each organ; mean ± SD for <span class="html-italic">n</span> = 3; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs on liver metastasis. (<b>A</b>) Timeline of EV and cell administrations. (<b>B</b>) Effect of colon26EVs; number of mice PBS: <span class="html-italic">n</span> = 7; colon26EVs: <span class="html-italic">n</span> = 6. (<b>C</b>) Effect of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs; number of mice PBS <span class="html-italic">n</span> = 5 (2 of 7 mice died within 2 weeks after the injection of <span class="html-italic">Nanog</span><sup>+</sup>colon26 cells); <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs: <span class="html-italic">n</span> = 9.</p>
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<p>Effects of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs on liver metastasis. (<b>A</b>) Timeline of EV and cell administrations. (<b>B</b>) Effect of colon26EVs; number of mice PBS: <span class="html-italic">n</span> = 7; colon26EVs: <span class="html-italic">n</span> = 6. (<b>C</b>) Effect of <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs; number of mice PBS <span class="html-italic">n</span> = 5 (2 of 7 mice died within 2 weeks after the injection of <span class="html-italic">Nanog</span><sup>+</sup>colon26 cells); <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs: <span class="html-italic">n</span> = 9.</p>
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<p>Effects of EVs on phagocytic activity of J774.1 cells. (<b>A</b>) Flow cytograms of J774.1 cells containing PKH26-EVs and/or FITC-MBs. PKH26, Ex: 551 nm, Em: 567 nm; FITC, Ex: 498 nm, Em: 517 nm. Plot colors: violet in P1, red in P2, blue in P3, green in P4. (<b>B</b>) Phagocytic activity, mean ± SD for <span class="html-italic">n</span> = 3. (<b>C</b>) Uptake of MBs by J774.1 single cells analyzed by fluorescent microscopy. Red arrows indicate MBs. (<b>D</b>) Number of MBs taken up per cell. °: outliers. PBS: <span class="html-italic">n</span> = 377, mean = 1.81, median = 1. colon26EVs: <span class="html-italic">n</span> = 367, mean = 1.65, median = 1. <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs: <span class="html-italic">n</span> = 420, mean = 2.19, median = 2. *: <span class="html-italic">p</span> &lt; 0.05; ***: <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>) Expression of macrophage marker (<span class="html-italic">CD80</span>) determined by qPCR; mean ± SD for <span class="html-italic">n</span> = 3. ***: <span class="html-italic">p</span> &lt; 0.001. (<b>F</b>) Expression of macrophage marker (<span class="html-italic">CD163</span>) determined by qPCR; mean ± SD for <span class="html-italic">n</span> = 3.</p>
Full article ">Figure 6 Cont.
<p>Effects of EVs on phagocytic activity of J774.1 cells. (<b>A</b>) Flow cytograms of J774.1 cells containing PKH26-EVs and/or FITC-MBs. PKH26, Ex: 551 nm, Em: 567 nm; FITC, Ex: 498 nm, Em: 517 nm. Plot colors: violet in P1, red in P2, blue in P3, green in P4. (<b>B</b>) Phagocytic activity, mean ± SD for <span class="html-italic">n</span> = 3. (<b>C</b>) Uptake of MBs by J774.1 single cells analyzed by fluorescent microscopy. Red arrows indicate MBs. (<b>D</b>) Number of MBs taken up per cell. °: outliers. PBS: <span class="html-italic">n</span> = 377, mean = 1.81, median = 1. colon26EVs: <span class="html-italic">n</span> = 367, mean = 1.65, median = 1. <span class="html-italic">Nanog</span><sup>+</sup>colon26EVs: <span class="html-italic">n</span> = 420, mean = 2.19, median = 2. *: <span class="html-italic">p</span> &lt; 0.05; ***: <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>) Expression of macrophage marker (<span class="html-italic">CD80</span>) determined by qPCR; mean ± SD for <span class="html-italic">n</span> = 3. ***: <span class="html-italic">p</span> &lt; 0.001. (<b>F</b>) Expression of macrophage marker (<span class="html-italic">CD163</span>) determined by qPCR; mean ± SD for <span class="html-italic">n</span> = 3.</p>
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<p>A volcano plot of miRNAs. Fold change (Fc) and p-value in statistical significance; (<span style="color:red">●</span>) Fc ≥ 1.5 and <span class="html-italic">p</span> &lt; 0.05, (<span style="color:#0066FF">●</span>) Fc ≤ 1/1.5 and <span class="html-italic">p</span> &lt; 0.05, (<span style="color:#00B050">●</span>) 1/1.5 &lt; Fc &lt; 1.5 and <span class="html-italic">p</span> &lt; 0.05, (<b><span style="color:red">○</span></b>) Fc ≥ 1.5 and <span class="html-italic">p</span> ≥ 0.05, (<b><span style="color:#0066FF">○</span></b>) Fc ≤ 1/1.5 and <span class="html-italic">p</span> ≥ 0.05, (<b><span style="color:#00B050">○</span></b>) 1/1.5 &lt; Fc &lt; 1.5 and <span class="html-italic">p</span> ≥ 0.05, where Log<sub>2</sub>(1.5) = 0.585, Log<sub>2(</sub>1/1.5) = −0.585, −Log<sub>10</sub>(0.05) = 1.301.</p>
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