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13 pages, 4948 KiB  
Review
ETS Transcription Factors in Immune Cells and Immune-Related Diseases
by Yaxu Yang, Xue Han, Lijun Sun, Fangyu Shao, Yue Yin and Weizhen Zhang
Int. J. Mol. Sci. 2024, 25(18), 10004; https://doi.org/10.3390/ijms251810004 (registering DOI) - 17 Sep 2024
Abstract
The development, differentiation, and function of immune cells are precisely regulated by transcription factors. The E26 transformation-specific (ETS) transcription factor family is involved in various physiological and pathological processes by regulating cell proliferation, differentiation, and apoptosis. Emerging evidence has suggested that ETS family [...] Read more.
The development, differentiation, and function of immune cells are precisely regulated by transcription factors. The E26 transformation-specific (ETS) transcription factor family is involved in various physiological and pathological processes by regulating cell proliferation, differentiation, and apoptosis. Emerging evidence has suggested that ETS family proteins are intimately involved in the development and function of immune cells. This review summarizes the role of the ETS family in immune cells and immune-related disorders. Seven transcription factors within the ETS family, including PU.1, ETV5, ETV6, ETS1/2, ELK3, and ELF1, play essential roles in the development and function of T cells, B cells, macrophages, neutrophils, and dendritic cells. Furthermore, they are involved in the occurrence and development of immune-related diseases, including tumors, allergies, autoimmune diseases, and arteriosclerosis. This review is conducive to a comprehensive overview of the role of the ETS family in immune cells, and thus is informative for the development of novel therapeutic strategies targeting the ETS family for immune-related diseases. Full article
(This article belongs to the Special Issue Molecular Regulation in Inflammatory and Autoimmune Diseases)
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Figure 1

Figure 1
<p>Homology tree of human ETS transcription factors. ETS factors mentioned in this review are shown in red. Neighbor-Joining test and MEGA11 software were used for the protein sequence alignment.</p>
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<p>ETS family’s involvement in immune cells. <a href="#ijms-25-10004-f002" class="html-fig">Figure 2</a> was drawn by Figdraw.</p>
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<p>The expression of ETS transcription factors in immune cells. PU.1 available from <a href="https://www.proteinatlas.org/ENSG00000066336-SPI1/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000066336-SPI1/immune+cell</a>. ETV5 available from <a href="https://www.proteinatlas.org/ENSG00000244405-ETV5/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000244405-ETV5/immune+cell</a>. ETV6 available from <a href="https://www.proteinatlas.org/ENSG00000139083-ETV6/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000139083-ETV6/immune+cell</a>. ETS1 available from <a href="https://www.proteinatlas.org/ENSG00000134954-ETS1/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000134954-ETS1/immune+cell</a>. ETS2 available from <a href="https://www.proteinatlas.org/ENSG00000157557-ETS2/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000157557-ETS2/immune+cell</a>. ELK3 available from <a href="https://www.proteinatlas.org/ENSG00000111145-ELK3/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000111145-ELK3/immune+cell</a>. ELF1 available from <a href="https://www.proteinatlas.org/ENSG00000120690-ELF1/immune+cell" target="_blank">https://www.proteinatlas.org/ENSG00000120690-ELF1/immune+cell</a> (all accessed on 14 September 2024).</p>
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<p>ETS family in immune cells. In Th17 cells, STAT3 directly binds to the ETV5 promoter to activate the expression of ETV5. ETV5 directly facilitates the expression of IL-17a and IL-17f. In Th2 cells, EVT5 promotes the production of IL-10. In Th1 cells, ETV5 enhances the level of IFN-γ. In Th9 cells, ETV5 and PU.1 promote the production of IL-9. PU.1 regulates early B-cell development and differentiation. In macrophages, ELK3 inhibits the expression of NOS2 and HO-1. ETS2 inhibits the expression of IL-6. Phosphorylated PU.1 initiates transcription of COX2 and ROS. LPS induces the formation of a complex between PU.1 and c-Jun promoting transcription. In bacteria-infected macrophages, PU.1 and ETS2 significantly enhance the activation of BCL-x. CRL4 targets the ETV5, which is degraded by the ubiquitin–proteasome system. In neutrophils, PU.1 activates the transcription of MRP1S and DAPK2. PU.1 also promotes neutrophil nuclear division by regulating the transcription of LBR. In cDCs, PU.1 activates the transcription of CCR7, CD11c, OX40L, DC-SIGN, and CIITA pI and forms a heterodimer with IRF8 promoting CIITA pI expression. In pDCs, PU.1 activates pIII by directly binding to the CIITA pIII promoter. Scheme 1. HO-1; Cyclooxygenase 2, COX2; Reactive oxygen species, ROS; Lipopolysaccharides, LPS; B-cell lymphoma, BCL; Cullin-RING E3 ubiquitin ligases, CRL4; Microtubule-associated protein 1S, MRP1S; Death-associated protein kinase 2, DAPK2; Laminin B receptor, LBR; Conventional DC, cDC; Plasmacytoid dendritic, pDC; C-C Motif Chemokine Receptor 7, CCR7; OX40 ligand, OX40L; Dendritic cell-specific ICAM-grabbing non-integrin, DC-SIGN; Interferon regulatory factor 8, IRF8; Class II trans-activator, CIITA.</p>
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17 pages, 7417 KiB  
Article
Anti-Tumor Immunity to Patient-Derived Breast Cancer Cells by Vaccination with Interferon-Alpha-Conditioned Dendritic Cells (IFN-DC)
by Caterina Lapenta, Stefano Maria Santini, Celeste Antonacci, Simona Donati, Serena Cecchetti, Patrizia Frittelli, Piera Catalano, Francesca Urbani, Iole Macchia, Massimo Spada, Sara Vitale, Zuleika Michelini, Domenico Cristiano Corsi, Ann Zeuner, Rosanna Dattilo and Manuela Tamburo De Bella
Vaccines 2024, 12(9), 1058; https://doi.org/10.3390/vaccines12091058 (registering DOI) - 17 Sep 2024
Abstract
Background: Breast cancer represents one of the leading causes of death among women. Surgery can be effective, but once breast cancer has metastasized, it becomes extremely difficult to treat. Conventional therapies are associated with substantial toxicity and poor efficacy due to tumor heterogeneity, [...] Read more.
Background: Breast cancer represents one of the leading causes of death among women. Surgery can be effective, but once breast cancer has metastasized, it becomes extremely difficult to treat. Conventional therapies are associated with substantial toxicity and poor efficacy due to tumor heterogeneity, treatment resistance and disease relapse. Moreover, immune checkpoint blockade appears to offer limited benefit in breast cancer. The poor tumor immunogenicity and the immunosuppressive tumor microenvironment result in scarce T-cell infiltration, leading to a low response rate. Thus, there is considerable interest in the development of improved active immunotherapies capable of sensitizing a patient’s immune system against tumor cells. Methods: We evaluated the in vitro anti-tumor activity of a personalized vaccine based on dendritic cells generated in the presence of interferon (IFN)-α and granulocyte-macrophage colony-stimulating factor (IFN-DC) and loaded with an oxidized lysate from autologous tumor cells expanded as 3D organoid culture maintaining faithful tumor antigenic profiles. Results: Our findings demonstrate that stimulation of breast cancer patients’ lymphocytes with autologous IFN-DC led to efficient Th1-biased response and the generation in vitro of potent cytotoxic activity toward the patients’ own tumor cells. Conclusions: This approach can be potentially applied in association with checkpoint blockade and chemotherapy in the design of new combinatorial therapies for breast cancer. Full article
(This article belongs to the Section Cancer Vaccines and Immunotherapy)
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Figure 1

Figure 1
<p>Phenotypic and functional analysis of peripheral blood lymphocytes (PBL) stimulated with HOCl-oxidized MCF-7 breast tumor cell lysate. (<b>a</b>) Dot-plot analysis of CD4+ and CD8+ cells as determined by flow cytometry in PBL stimulated with IFN-DC. PBL isolated from HLA-A2+ healthy blood donors were cultured with autologous IFN-DC (IFN-DC/PBL ratio of 1:4) pulsed with MCF-7 tumor cell lysate, as described in <a href="#sec2-vaccines-12-01058" class="html-sec">Section 2</a>. Representative results of three independent experiments are shown. (<b>b</b>) Cytokine production (IFN-γ, TNF-α and IL-10) in culture supernatants as evaluated by ELISA on days 7 and 14 of co-culture. (<b>c</b>) Degranulation assay as a surrogate evaluation of cytotoxic activity by detection of CD107a membrane expression and intracellular IFN-γ production in CD8+ and natural killer (NK) cells. Representative dot-plot analysis in electronically gated CD8+CD3+ and CD56+CD3- cells in PBL co-cultured with tumor cell-loaded IFN-DC. On day 21 of culture, PBL were restimulated with MCF-7 or K562 target cell lines for 4 h at 37 °C (E:T ratio of 2:1) (see <a href="#sec2-vaccines-12-01058" class="html-sec">Section 2</a>). Dot-plots show CD107a membrane exposure and IFN-γ expression in electronically gated CD8+CD3+ and CD56+CD3- lymphocytes in response to the indicated target cells. Results from one representative experiment out of four are shown. (<b>d</b>) Representative cytotoxicity assay against NK-sensitive K562 and MCF-7 target cell lines as evaluated by a Calcein-AM assay (see <a href="#sec2-vaccines-12-01058" class="html-sec">Section 2</a>) at different E:T ratios. Data are mean ± SD of a triplicate assay of PBL derived from an HLA-A2+ donor.</p>
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<p>Evaluation of tumor-growth inhibition by in vivo immunization of hu-PBL-NSG mice with IFN-DC loaded with HOCl-oxidized tumor cell lysate. (<b>a</b>) Vaccination schedule. Mice were reconstituted with HLA-A2+ PBL as soon as the implanted tumors became detectable by in vivo bioluminescence imaging (10–11 days). Humanized mice were then randomized into treatment and control groups. (<b>b</b>) Quantitative evaluation of tumor cell growth by bioluminescence analysis. Tumor burden was detected by in vivo non-invasive imaging of the firefly luciferase expressing MCF-7 cells after intraperitoneal luciferin injection. Tumor bioluminescence intensity was plotted in pseudocolor over black/white photographs and quantified as total flux in photons/seconds. Graph represents mean total flux of MCF-7 cell growth rate in hu-PBL-NSG mice immunized as described. The data are presented as mean ± SEM. The difference in tumor growth was highly statistically significant only at the last time point, day 47 (** <span class="html-italic">p</span> &lt; 0.01 by Mann–Whitney test). (<b>c</b>) Representative tumor burden images of the two groups (CTR vs. vaccine) at different time points by IVIS imaging system. (<b>d</b>) Evaluation of IFN-γ levels in mouse sera collected at the time of sacrifice.</p>
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<p>Isolation and characterization of patient-derived breast cancer organoids (PDBCOs). (<b>a</b>) Representative bright-field images of 5 PDBCOs used for the study, showing different structures: cohesive and discohesive organoids, dense and solid (PBR-13, PBR-14, PBR-16, PBR-17), cohesive organoids, cystic and grape-like (P-BR-22). Scale bar, 100 µm. (<b>b</b>) Comparative histological and immunohistochemical images of BC tissues and derived organoid lines. Shown are representative examples of H&amp;E staining and IHC of either HR or HER-2 status for PBR-17. Scale bar, 200 µm. (<b>c</b>) Stacked bar chart indicating the percentage of PDBCO lines found positive (grey) and negative (black) by IHC for the receptor expression grouped per original tumor receptor status. (<b>d</b>) Bar graph displaying proliferation rate percentage of PDBCOs and corresponding parental tumors as quantified by Ki67 immunohistochemical staining. (<b>e</b>) Representative dot-plot graphs showing CK14 and CK8-18 expression in PBR-13, PBR-16 and PBR-22 (<b>f</b>) CLSM analyses of PFA-fixed PDBCOs stained for CK8-18, SMA and E-cadherin (green); 4′-6-Diamidino-2-phenylindole (DAPI) was used to counterstain nuclei (light blue). Several (&gt;50 organoids) were observed for each condition and representative images are shown. Scale bars, 10 µm.</p>
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<p>Isolation and characterization of breast cancer patient-derived metastatic cells (PDMCs). (<b>a</b>) Representative phase-contrast images of PDMCs from ascitic fluid (MBR-1, MBR-2) or pleural effusion (MBR-3, MBR-4) cultured in serum-free conditions. Scale bar, 100 µm. (<b>b</b>) CLSM analyses of PFA-fixed PDMCs stained for E-cadherin, vimentin Taz and beta-catenin (green); DAPI was used to counterstain nuclei (light blue). Several fields were observed for each condition and representative images are shown. Scale bars, 10 µm. (<b>c</b>) Dot-plots showing luminal CK8-18 and myoepithelial CK14 expression in PDMC lines. (<b>d</b>) Bar chart reporting percentages of CD44<sup>high</sup>CD24<sup>−/low</sup> phenotype in MBR-1, MBR-2, MBR-3 and MBR-4 lines obtained from ascitic fluid or pleural effusion of breast cancer metastatic patients. Percentages, referring to CD44<sup>high</sup>CD24<sup>−/low</sup> positive cells, were determined by setting the gate on the isotype control from at least two independent FACS stainings.</p>
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<p>Characterization of PBL cultures from breast tumor patients stimulated with IFN-DC loaded with HOCl-oxidized autologous tumor cell lysate. (<b>a</b>). Representative phenotypical analysis of IFN-DC obtained from breast tumor patients. IFN-DC were differentiated from peripheral blood monocytes as described in <a href="#sec2-vaccines-12-01058" class="html-sec">Section 2</a>. Partially mature CD11c+ IFN-DC ex-pressed high levels of the costimulatory molecules CD80 and CD86, as well as variable levels of the maturation marker CD83. (<b>b</b>) IL-12 release in supernatants collected from IFN-DC cultures on day 3 of differentiation. (<b>c</b>) Representative phenotypic analysis of PBL isolated from breast tumor patients and cultured with autologous IFN-DC loaded with HOCl-oxidized tumor cell lysate for 14 days. (<b>d</b>) Evaluation of CD4, CD8 and NK cell percentages in PBL from breast cancer patients before (T0) and after 14 days of culture (T14) with autologous IFN-DC. (<b>e</b>) Flow cytometric analysis of CD4 and CD8 cell memory subsets of freshly purified PBL from breast cancer patients and after 14 days of culture with autologous IFN-DC loaded with HOCl-oxidized tumor cell lysate. (<b>f</b>) Cytokine release (IFN-γ, TNF-α and IL-10) in culture supernatants as evaluated by ELISA on day 14 of co-cultures.</p>
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<p>Antitumor activity of PBL from breast cancer patients co-cultured with IFN-DC loaded with HOCl-oxidized autologous tumor cell lysate. PBL isolated from nine patient blood donors were cultured with IFN-DC pulsed with HOCl-oxidized tumor cell lysate for 14 days. (<b>a</b>) Degranulation activity of expanded effectors cells tested against autologous breast cancer target cells as detected by flow cytometry; analysis of CD107a membrane expression and intracellular IFN-γ. (<b>b</b>) Representative degranulation assay as assessed by dot-plot analysis of ectopic CD107a and IFN-γ expression in CD8+ and CD3-CD56+ NK cells derived from three representative breast cancer patients in response to autologous tumor cells. (<b>c</b>) Cytotoxic assay of PBL culture from breast cancer patients after in vitro culture for 14 days, tested against autologous breast cancer cells. (<b>d</b>) Phenotypic analysis of CD8 cells from patient PBR-22 (HLA-A2+) stimulated with IFN-DC loaded with HOCl-oxidized autologous tumor cell lysate for 14 days. (<b>e</b>) Cytotoxic activity of PBL from PBR-22 (HLA-A2+) as compared to PBR-13 (HLA-A2–) as determined by cytotoxic assay towards HLA-A2+ MCF-7 target cells. (<b>f</b>) Degranulation activity as determined by dot plot analysis of CD107a and IFN-γ expression in CD8+ and CD3-CD56+ NK cells from patients PBR-22 and P-BR13 toward MCF-7 target cells.</p>
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16 pages, 1043 KiB  
Review
Research Progress on Dendritic Cells in Hepatocellular Carcinoma Immune Microenvironments
by Wenya Li, Guojie Chen, Hailin Peng, Qingfang Zhang, Dengyun Nie, Ting Guo, Yinxing Zhu, Yuhan Zhang and Mei Lin
Biomolecules 2024, 14(9), 1161; https://doi.org/10.3390/biom14091161 - 16 Sep 2024
Viewed by 280
Abstract
Dendritic cells (DCs) are antigen-presenting cells that play a crucial role in initiating immune responses by cross-presenting relevant antigens to initial T cells. The activation of DCs is a crucial step in inducing anti-tumor immunity. Upon recognition and uptake of tumor antigens, activated [...] Read more.
Dendritic cells (DCs) are antigen-presenting cells that play a crucial role in initiating immune responses by cross-presenting relevant antigens to initial T cells. The activation of DCs is a crucial step in inducing anti-tumor immunity. Upon recognition and uptake of tumor antigens, activated DCs present these antigens to naive T cells, thereby stimulating T cell-mediated immune responses and enhancing their ability to attack tumors. It is particularly noted that DCs are able to cross-present foreign antigens to major histocompatibility complex class I (MHC-I) molecules, prompting CD8+ T cells to proliferate and differentiate into cytotoxic T cells. In the malignant progression of hepatocellular carcinoma (HCC), the inactivation of DCs plays an important role, and the activation of DCs is particularly important in anti-HCC immunotherapy. In this review, we summarize the mechanisms of DC activation in HCC, the involved regulatory factors and strategies to activate DCs in HCC immunotherapy. It provides a basis for the study of HCC immunotherapy through DC activation. Full article
19 pages, 1650 KiB  
Article
Evaluating Nanoparticulate Vaccine Formulations for Effective Antigen Presentation and T-Cell Proliferation Using an In Vitro Overlay Assay
by Dedeepya Pasupuleti, Priyal Bagwe, Amarae Ferguson, Mohammad N. Uddin, Martin J. D’Souza and Susu. M. Zughaier
Vaccines 2024, 12(9), 1049; https://doi.org/10.3390/vaccines12091049 - 13 Sep 2024
Viewed by 340
Abstract
Inducing T lymphocyte (T-cell) activation and proliferation with specificity against a pathogen is crucial in vaccine formulation. Assessing vaccine candidates’ ability to induce T-cell proliferation helps optimize formulation for its safety, immunogenicity, and efficacy. Our in-house vaccine candidates use microparticles (MPs) and nanoparticles [...] Read more.
Inducing T lymphocyte (T-cell) activation and proliferation with specificity against a pathogen is crucial in vaccine formulation. Assessing vaccine candidates’ ability to induce T-cell proliferation helps optimize formulation for its safety, immunogenicity, and efficacy. Our in-house vaccine candidates use microparticles (MPs) and nanoparticles (NPs) to enhance antigen stability and target delivery to antigen-presenting cells (APCs), providing improved immunogenicity. Typically, vaccine formulations are screened for safety and immunostimulatory effects using in vitro methods, but extensive animal testing is often required to assess immunogenic responses. We identified the need for a rapid, intermediate screening process to select promising candidates before advancing to expensive and time-consuming in vivo evaluations. In this study, an in vitro overlay assay system was demonstrated as an effective high-throughput preclinical testing method to evaluate the immunogenic properties of early-stage vaccine formulations. The overlay assay’s effectiveness in testing particulate vaccine candidates for immunogenic responses has been evaluated by optimizing the carboxyfluorescein succinimidyl ester (CFSE) T-cell proliferation assay. DCs were overlaid with T-cells, allowing vaccine-stimulated DCs to present antigens to CFSE-stained T-cells. T-cell proliferation was quantified using flow cytometry on days 0, 1, 2, 4, and 6 upon successful antigen presentation. The assay was tested with nanoparticulate vaccine formulations targeting Neisseria gonorrhoeae (CDC F62, FA19, FA1090), measles, H1N1 flu prototype, canine coronavirus, and Zika, with adjuvants including Alhydrogel® (Alum) and AddaVax™. The assay revealed robust T-cell proliferation in the vaccine treatment groups, with variations between bacterial and viral vaccine candidates. A dose-dependent study indicated immune stimulation varied with antigen dose. These findings highlight the assay’s potential to differentiate and quantify effective antigen presentation, providing valuable insights for developing and optimizing vaccine formulations. Full article
(This article belongs to the Special Issue Advances in the Use of Nanoparticles for Vaccine Platform Development)
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Figure 1
<p>Live cell imaging of DAPI-stained naïve T-cells interacting with activated dendritic cells. (<b>A</b>): Overview of the culture showing DAPI-stained T-cells (blue) interacting with activated dendritic cells that are stimulated with ICG-coated BSA MPs (green) across the field. Scale bar: 100 µm. (<b>B</b>): Close-up view highlighting a DAPI-stained T-cell engaging with a dendritic cell, indicated by the black arrow. Scale bar: 100 µm. (<b>C</b>): Magnified image displaying multiple T-cells in the process of interacting with dendritic cells. Black arrows indicate T-cells undergoing division. Scale bar: 100 µm. (<b>D</b>): Detailed image of T-cells post-division, as indicated by black arrows, continuing their interaction with dendritic cells. Scale bar: 100 µm.</p>
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<p>Representative flow cytometry data of T-lymphocyte profiling. (<b>A</b>). Gating strategy for separating T lymphocytes from the forward scattering vs. side scattering plot. T-cells were gated, capturing 16.5% of the total population of the scatter plot. (<b>B</b>). Singlets are shown on the forward scattering a vs. forward scattering height plot from the T lymphocyte gating. (<b>C</b>). Histograms gated 1, 2, 3, and 4 according to daughter T-cell proliferation over time intervals: 0–1, 1–2, 2–4, and 4–6 days, respectively. Gates were established in accordance with the proliferation pattern of bacterial and viral-based vaccine candidates. Gates were left unchanged for the corresponding blank MP/NP groups.</p>
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<p>Quantitative comparisons of CFSE (FITC-A filter) expressions due to T-cells proliferated as days passed. CFSE is expressed by the proliferating T-cells in response to antigen presentation by the DCs upon stimulation by various treatment groups. (<b>A</b>). Comparison of all blank groups involved in the experiment, including blank CFSE-stained T-cells only, blank BSA MPs, and blank PLGA NPs. (<b>B</b>). comparison of all viral antigen-based vaccine candidates. (<b>C</b>). comparison of all bacterial antigen-based vaccine candidates. All treatments are at 200 µg per well dose. Data are expressed as mean ± SEM, ordinary one-way ANOVA test, post-hoc Tukey’s multiple comparison test. ns, non-significant, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Dose-dependent study results quantifying T-cell proliferation against various concentrations of vaccine candidates. (<b>A</b>). T-cell proliferation analysis measured on days 1, 2, 4, and 6 when treated with vaccine candidate against <span class="html-italic">N. gonorrhoeae</span> strain FA1090 at concentrations of 200 µg, 160 µg, 120 µg, 80 µg, and 40 µg vaccine MPs per well. (<b>B</b>). T-cell proliferation analysis was measured on days 1, 2, 4, and 6 when treated with a vaccine candidate against the measles virus at concentrations of 200 µg, 160 µg, 120 µg, 80 µg, and 40 µg vaccine NPs per well. (<b>C</b>,<b>D</b>). T-cell proliferation trends quantified in response to H1N1 virus particle vaccine candidate and <span class="html-italic">N. gonorrhoeae</span> strain CDC F62 bacterial particle vaccine candidates on day 6. Both were tested at concentrations of 200 µg, 160 µg, 120 µg, 80 µg, and 40 µg per well on day 6. Data are expressed as mean ± SEM, one-way ANOVA, post hoc Tukey’s multiple comparisons test; ns, non-significant, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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25 pages, 4233 KiB  
Article
Characterization of the Immune-Modulating Properties of Different β-Glucans on Myeloid Dendritic Cells
by Hannah Rainer, Alexandra Goretzki, Yen-Ju Lin, Hannah Ruth Schiller, Maren Krause, Sascha Döring, Daniel Strecker, Ann-Christine Junker, Sonja Wolfheimer, Masako Toda, Stephan Scheurer and Stefan Schülke
Int. J. Mol. Sci. 2024, 25(18), 9914; https://doi.org/10.3390/ijms25189914 (registering DOI) - 13 Sep 2024
Viewed by 327
Abstract
In allergen-specific immunotherapy, adjuvants are explored for modulating allergen-specific Th2 immune responses to re-establish clinical tolerance. One promising class of adjuvants are β-glucans, which are naturally derived sugar structures and components of dietary fibers that activate C-type lectin (CLR)-, “Toll”-like receptors (TLRs), and [...] Read more.
In allergen-specific immunotherapy, adjuvants are explored for modulating allergen-specific Th2 immune responses to re-establish clinical tolerance. One promising class of adjuvants are β-glucans, which are naturally derived sugar structures and components of dietary fibers that activate C-type lectin (CLR)-, “Toll”-like receptors (TLRs), and complement receptors (CRs). We characterized the immune-modulating properties of six commercially available β-glucans, using immunological (receptor activation, cytokine secretion, and T cell modulating potential) as well as metabolic parameters (metabolic state) in mouse bone marrow-derived myeloid dendritic cells (mDCs). All tested β-glucans activated the CLR Dectin-1a, whereas TLR2 was predominantly activated by Zymosan. Further, the tested β-glucans differentially induced mDC-derived cytokine secretion and activation of mDC metabolism. Subsequent analyses focusing on Zymosan, Zymosan depleted, β-1,3 glucan, and β-1,3 1,6 glucan revealed robust mDC activation with the upregulation of the cluster of differentiation 40 (CD40), CD80, CD86, and MHCII to different extents. β-glucan-induced cytokine secretion was shown to be, in part, dependent on the activation of the intracellular Dectin-1 adapter molecule Syk. In co-cultures of mDCs with Th2-biased CD4+ T cells isolated from birch allergen Bet v 1 plus aluminum hydroxide (Alum)-sensitized mice, these four β-glucans suppressed allergen-induced IL-5 secretion, while only Zymosan and β-1,3 glucan significantly suppressed allergen-induced interferon gamma (IFNγ) secretion, suggesting the tested β-glucans to have distinct effects on mDC T cell priming capacity. Our experiments indicate that β-glucans have distinct immune-modulating properties, making them interesting adjuvants for future allergy treatment. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Allergy and Asthma: 3rd Edition)
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Figure 1
<p>β-glucans differ in their activation of mDC metabolism and the secretion of pro- and anti-inflammatory cytokines. Bone marrow of C57BL/6 mice was isolated, differentiated into mDCs for 8 days, and subsequently stimulated with either 20 µg/mL of the indicated β-glucans or 1 µg/mL of LPS as a positive control (<b>A</b>). The Warburg Effect was measured at OD<sub>570nm</sub>, and the inverted values were normalized to the unstimulated controls (<b>B</b>). The glucose concentration in the cell culture supernatant was determined using the Glucose (GO) assay kit and measuring the absorption at OD<sub>540nm</sub> (<b>C</b>). The secretion of the cytokines IL-6, IL-1β, IL-10, and IL-12p70 was measured via sandwich ELISA at OD<sub>450nm</sub>. Data are mean results ± SD of three independent experiments (<b>D</b>). Statistical comparison was performed by 1-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: no indication = not significant <span class="html-italic">p</span>-value &gt; 0.05, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001, or **** <span class="html-italic">p</span>-value &lt; 0.0001.</p>
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<p>Stimulation of mDCs with β-glucans results in increased metabolic activity. Bone marrow of C57BL/6 mice was isolated, differentiated into mDCs for 8 days, and subsequently analyzed in extracellular flux assays using Agilent Seahorse technology (<b>A</b>). mDCs were seeded overnight into Seahorse XF96 cell culture microplates, stimulated with increasing doses of the indicated β-glucan for 14 cycles (84 min), and analyzed for ECAR and OCR. Afterward, ATP synthase, ETC, and glycolysis were inhibited by sequentially injecting oligomycin, Rotenone/antimycin A (Rot/AA), and 2-deoxyglucose (2-DG), respectively, for 8 cycles (48 min) each. Data are representative of three independent experiments (<b>B</b>). The red arrow indicates the measurement cycle used for statistical analysis. Data are mean results ± SD of three independent experiments (<b>C</b>). Statistical comparison was performed by 1-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: no indication = not significant <span class="html-italic">p</span>-value &gt; 0.05, *** <span class="html-italic">p</span>-value &lt; 0.001, or **** <span class="html-italic">p</span>-value &lt; 0.0001. Abbreviations: ECAR: extracellular acidification rate, OCR: oxygen consumption rate, Rot/AA: rotenone/antimycin A, 2-DG: 2-deoxyglucose.</p>
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<p>β-glucans upregulate the expression of the pattern recognition receptors TLR2 and Dectin-1 on mDCs. Bone marrow of C57BL/6 mice was isolated and differentiated for 8 days into mDCs that were subsequently stimulated with either 12 µg/mL of the indicated β-glucans or 10 µg/mL LPS as a positive control for 24 h (<b>A</b>). Cells were harvested, and surface expression of the indicated pattern recognition receptors was analyzed via flow cytometry. Stimulated samples (colored) were compared to either unstimulated controls (light grey filled) or fluorescence-minus-one (FMO)-stained cells (dashed lines). Co-expression of CD11b and CD18 forming the complement receptor 3 on LPS and β-glucan-stimulated mDCs was investigated by flow cytometry (<b>D</b>). FMOs are shown in blue for PE-Cy7 and green for PE, respectively. Data are representative results from one out of three independent experiments (<b>B</b>,<b>D</b>) or geometric mean fluorescence intensities (Geo. MFI) from three independent experiments (<b>C</b>,<b>E</b>). Dashed lines indicate the expression level of the unstimulated control. Statistical comparison was performed by 1-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: no indication = not significant and <span class="html-italic">p</span>-value &gt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, or **** <span class="html-italic">p</span>-value &lt; 0.0001. Abbreviations: MFI: mean fluorescence intensity, FMO: fluorescence-minus-one.</p>
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<p>β-glucans upregulate the expression of MHCII; surface activation-, and co-stimulatory markers on mDCs. Bone marrow of C57BL/6 mice was isolated and differentiated for 8 days into mDCs that were subsequently stimulated with either 12 µg/mL of the indicated β-glucans or 10 µg/mL LPS as a positive control for 24 h (<b>A</b>). Cells were harvested, and surface expression of MHCII, the indicated activation markers, and co-stimulatory molecules were analyzed via flow cytometry. Stimulated samples (colored) were compared to either unstimulated controls (light grey filled) or fluorescence-minus-one (FMO)-stained cells (dashed lines). Data are representative results from one out of three independent experiments (<b>B</b>) or quantified as geometric mean fluorescence intensities (Geo. MFI) from three independent experiments (<b>C</b>). Dashed lines indicate the expression level of the unstimulated control. Statistical comparison was performed by 1-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: no indication = not significant and <span class="html-italic">p</span>-value &gt; 0.05, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001, or **** <span class="html-italic">p</span>-value &lt; 0.0001. Abbreviations: MFI: mean fluorescence intensity.</p>
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<p>β-glucan-induced cytokine secretion in part depends on Syk-activation. Bone marrow of C57BL/6 mice was isolated, differentiated into mDCs for 8 days, pre-treated with 0.5 µM of the Syk-inhibitor TAK-659 for 90 min, and subsequently stimulated with either 12 µg/mL of the indicated β-glucans or 10 µg/mL of LPS as a positive control for additional 72 h (<b>A</b>). Bars with solid filling: stimulation without inhibitor pre-treatment, dashed bars: pre-treatment with 0.5 µM TAK-659 followed by the indicated stimulation. The Warburg Effect was measured at OD<sub>570nm</sub>, and the inverted values were normalized to the unstimulated controls (<b>B</b>). The glucose concentration in the cell culture supernatant was determined by using the Glucose (GO) assay kit and measuring the absorption at OD<sub>540nm</sub> (<b>C</b>). The secretion of the cytokines IL-6, IL-1β, IL-10, and IL-12p70 was measured via sandwich ELISA at OD<sub>450nm</sub> (<b>D</b>). Data are mean results of three independent experiments. Statistical comparison was performed by 2-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: ns = not significant and <span class="html-italic">p</span>-value &gt; 0.05, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001, or **** <span class="html-italic">p</span>-value &lt; 0.0001.</p>
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<p>β-glucan-stimulated mDCs can suppress both IL-5 and IFNγ production from Th2-primed Bet v 1-specific CD4<sup>+</sup> T cells. Bone marrow of BALB/c mice was isolated and differentiated into mDCs for 8 days. CD4<sup>+</sup> T cells were isolated from BALB/c mice that were previously sensitized twice with 10 µg of the major birch pollen allergen Bet v 1 and 2 mg Alum i.p. The differentiated mDCs and the isolated T cells were co-cultured in 48-well plates and either stimulated with 8 µg of the respective β-glucan (lighter colors) or re-stimulated with 4.3 µg Bet v 1 in the presence of 8 µg of the respective β-glucan (darker colors) for additional 72 h (<b>A</b>). The Warburg Effect was measured at OD<sub>570nm</sub> normalized to the unstimulated controls with or without Bet v 1, respectively (<b>B</b>). Secretion of IL-2, IL-5, IFNγ, IL-10, and IL-13 were determined via sandwich ELISA. Data are mean results ± SD of three independent experiments. Statistical comparison was performed by 1-way ANOVA with correction for multiple comparisons according to Dunnett and indicated as follows: no indication = not significant and <span class="html-italic">p</span>-value &gt; 0.05, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001, or **** <span class="html-italic">p</span>-value &lt; 0.0001.</p>
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27 pages, 23025 KiB  
Article
Disulfidptosis: A New Target for Parkinson’s Disease and Cancer
by Tingting Liu, Xiangrui Kong and Jianshe Wei
Curr. Issues Mol. Biol. 2024, 46(9), 10038-10064; https://doi.org/10.3390/cimb46090600 - 12 Sep 2024
Viewed by 311
Abstract
Recent studies have uncovered intriguing connections between Parkinson’s disease (PD) and cancer, two seemingly distinct disease categories. Disulfidptosis has garnered attention as a novel form of regulated cell death that is implicated in various pathological conditions, including neurodegenerative disorders and cancer. Disulfidptosis involves [...] Read more.
Recent studies have uncovered intriguing connections between Parkinson’s disease (PD) and cancer, two seemingly distinct disease categories. Disulfidptosis has garnered attention as a novel form of regulated cell death that is implicated in various pathological conditions, including neurodegenerative disorders and cancer. Disulfidptosis involves the dysregulation of intracellular redox homeostasis, leading to the accumulation of disulfide bonds and subsequent cell demise. This has sparked our interest in exploring common molecular mechanisms and genetic factors that may be involved in the relationship between neurodegenerative diseases and tumorigenesis. The Gene4PD database was used to retrieve PD differentially expressed genes (DEGs), the biological functions of differential expression disulfidptosis-related genes (DEDRGs) were analyzed, the ROCs of DEDRGs were analyzed using the GEO database, and the expression of DEDRGs was verified by an MPTP-induced PD mouse model in vivo. Then, the DEDRGs in more than 9000 samples of more than 30 cancers were comprehensively and systematically characterized by using multi-omics analysis data. In PD, we obtained a total of four DEDRGs, including ACTB, ACTN4, INF2, and MYL6. The enriched biological functions include the regulation of the NF-κB signaling pathway, mitochondrial function, apoptosis, and tumor necrosis factor, and these genes are rich in different brain regions. In the MPTP-induced PD mouse model, the expression of ACTB was decreased, while the expression of ACTN4, INF2, and MYL6 was increased. In pan-cancer, the high expression of ACTB, ACTN4, and MYL6 in GBMLGG, LGG, MESO, and LAML had a poor prognosis, and the high expression of INF2 in LIHC, LUAD, UVM, HNSC, GBM, LAML, and KIPAN had a poor prognosis. Our study showed that these genes were more highly infiltrated in Macrophages, NK cells, Neutrophils, Eosinophils, CD8 T cells, T cells, T helper cells, B cells, dendritic cells, and mast cells in pan-cancer patients. Most substitution mutations were G-to-A transitions and C-to-T transitions. We also found that miR-4298, miR-296-3p, miR-150-3p, miR-493-5p, and miR-6742-5p play important roles in cancer and PD. Cyclophosphamide and ethinyl estradiol may be potential drugs affected by DEDRGs for future research. This study found that ACTB, ACTN4, INF2, and MYL6 are closely related to PD and pan-cancer and can be used as candidate genes for the diagnosis, prognosis, and therapeutic biomarkers of neurodegenerative diseases and cancers. Full article
(This article belongs to the Section Bioinformatics and Systems Biology)
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<p>The DEDRG-enriched GO terms and KEGG pathways. (<b>A</b>) GOBP, GOCC, and GOMF analysis. (<b>B</b>) Signaling pathway enrichment analysis. Red represents DEDRGs, green represents biological process, purple represents molecular function, orange represents cellular component, and blue represents signaling pathways.</p>
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<p>Spatio-temporal expression profiles of (<b>A</b>) ACTB, (<b>B</b>) ACTN4, (<b>C</b>) INF2, and (<b>D</b>) MYL6 retrieved from BrainSpan. The darker the blue color, the higher the protein expression level in the brain region.</p>
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<p>Diagnostic value of DEDRGs in (<b>A</b>) 16 PD and 9 control subjects from the substantia nigra postmortem brain from the GSE7621 dataset; (<b>B</b>) 8 PD and 9 control subjects from the substantia nigra of postmortem brains from the GSE20163 dataset; (<b>C</b>) 6 PD and 5 control subjects from substantia nigra samples from the GSE20164 dataset; (<b>D</b>) control Braak α-synuclein Stage 0: 8 samples; Braak α-synuclein stages 1–2: 5 samples; Braak α-synuclein stages 3–4: 7 samples; Braak α-synuclein stages 5–6: 8 samples from the GSE49036 dataset; (<b>E</b>) 8 PD and 8 control subjects from peripheral mononuclear blood cells from the GSE22491 dataset; (<b>F</b>) 233 healthy controls and 205 idiopathic PD patients from whole blood from the GSE99039 dataset. (<b>G</b>) The expression of DEDRGs from the GSE49036 dataset at different stages. Gene ID: 200801_x_at, 213867_x_at, 224594_x_at, AFFX-HSAC07/X00351_3_at, AFFX-HSAC07/X00351_5_at, AFFX-HSAC07/X00351_M_at, 200601_at, 218144_s_at, 222534_s_at, 222535_at, 224469_s_at, 212082_s_at, 214002_at.</p>
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<p>Validity verification of DEDRGs. (<b>A</b>) Validation of DEDRGs by Western blotting. (<b>B</b>) Statistical plots of SLC7A11, ACTB, ACTN4, INF2, and MYL6. Compared with the saline group, ns = no significance, * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p</span> &lt; 0.01. <span class="html-italic">n</span> = 3. (<b>C</b>) Location of ACTN4 and INF2 proteins in cells from the HPA database: green represents the target protein, red represents microtubules, yellow represents the endoplasmic reticulum, and blue represents the nucleus (scale bar, 20 µm).</p>
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<p>Box plot of differential expression of DEDRGs between normal and tumor samples. (<b>A</b>) The differential expression of ACTB in pan-cancer. (<b>B</b>) The differential expression of ACTN4 in pan-cancer. (<b>C</b>) The differential expression of INF2 in pan-cancer. (<b>D</b>) The differential expression of MYL6 in pan-cancer. Compared with the normal samples, * <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>Pan-cancer prognostic analysis of DEDRGs using univariate Cox regression, including (<b>A</b>) ACTB, (<b>B</b>) ACTN4, (<b>C</b>) INF2, and (<b>D</b>) MYL6.</p>
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<p>Survival analysis of DEDRG expression in pan-cancer. (<b>A</b>–<b>H</b>) Survival curves of ACTB in GBMLGG, LGG, MESO, KIRC, UVM, HNSC, LIHC, LUAD. (<b>I</b>–<b>N</b>) Survival curves of ACTN4 in GBMLGG, LGG, MESO, PAAD, LUAD, KIRC. (<b>O</b>–<b>R</b>) Survival curves of INF2 in LIHC, HNSC, GBM, LAML. (<b>S</b>–<b>X</b>) Survival curves of MYL6 in GBMLGG, LGG, ACC, UVM, LAML, SARC.</p>
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<p>Pan-cancer immune infiltration analysis: (<b>A</b>) Immunoinfiltration analysis of ACTB in GBMLGG, LGG, MESO, KIRC, UVM, HNSC, LIHC, LUAD, and GBM. (<b>B</b>) Immunoinfiltration analysis of ACTN4 in GBMLGG, LGG, MESO, PAAD, LUAD, and KIRC. (<b>C</b>) Immunoinfiltration analysis of INF2 in LIHC, HNSC, GBM, and LAML. (<b>D</b>) Immunoinfiltration analysis of MYL6 in GBMLGG, ACC, LGG, UVM, LAML, and SARC. The correlation coefficient being positive indicates a positive correlation between two variables; a negative correlation coefficient indicates a negative correlation between two variables. The absolute value of the correlation coefficient represents the degree of correlation: 0–0.3 indicates weak or no correlation; 0.3–0.5 indicates weak correlation; 0.5–0.8 indicates moderate correlation; 0.8–1 indicates strong correlation. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Single-cell type analysis of DEDRGs, including ACTB, ACTN4, INF2, and MYL6, mainly from glandular epithelial cells, squamous epithelial cells, specialized epithelial cells, endocrine cells, neuronal cells, glial cells, germ cells, trophoblast cells, endothelial cells, muscle cells, adipocytes, pigment cells, mesenchymal cells, undifferentiated cells, and blood and immune cells.</p>
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<p>Gene mutation analysis of DEDRGs. (<b>A</b>) The pan-cancer mutation status of ACTB was determined using the cBioPortal tool. (<b>B</b>) ACTB base mutation frequency. (<b>C</b>) The pan-cancer mutation status of ACTN4 was determined using the cBioPortal tool. (<b>D</b>) ACTN4 base mutation frequency. (<b>E</b>) The pan-cancer mutation status of INF2 was determined using the cBioPortal tool. (<b>F</b>) INF2 base mutation frequency. (<b>G</b>) The pan-cancer mutation status of MYL6 was determined using the cBioPortal tool. (<b>H</b>) MYL6 base mutation frequency.</p>
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<p>Tumor pathological staining of ACTB in glioma, renal cancer, head and neck cancer, liver cancer, and lung cancer; ACTN4 in glioma, pancreatic cancer, lung cancer, and renal cancer; INF2 in liver cancer, head and neck cancer, and glioma; MYL6 in glioma (scale bar, 20 µm).</p>
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<p>Coexpression network of DEDRGs and target miRNAs.</p>
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<p>Binding mode of screened drugs to their targets by molecular docking. (<b>A</b>) The structure of cyclophosphamide. (<b>B</b>) The structure of ethinyl estradiol. (<b>C</b>) The structure of ACTB (3byh). (<b>D</b>) Molecular docking results of ACTB and cyclophosphamide. (<b>E</b>) Molecular docking results of ACTB and ethinyl estradiol.</p>
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9 pages, 2430 KiB  
Article
T-Cells Rich Classical Hodgkin Lymphoma, a Pathology Diagnostic Pitfall for Nodular Lymphocyte-Predominant Hodgkin Lymphoma; Case Series and Review
by Haneen Al-Maghrabi, Ghadeer Mokhtar and Ahmed Noorsaeed
Lymphatics 2024, 2(3), 168-176; https://doi.org/10.3390/lymphatics2030014 - 12 Sep 2024
Viewed by 311
Abstract
Background: Some cases of classic Hodgkin lymphoma (CHL) display similarities to nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) in terms of architecture, leading to potential challenges in diagnosis. However, these difficulties can be overcome by conducting a thorough set of immunohistochemical examinations. Objective: To [...] Read more.
Background: Some cases of classic Hodgkin lymphoma (CHL) display similarities to nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) in terms of architecture, leading to potential challenges in diagnosis. However, these difficulties can be overcome by conducting a thorough set of immunohistochemical examinations. Objective: To examine cases of T-cell-rich CHL that closely resemble the diagnosis of NLPHL, specifically pattern D, which can pose challenges in accurately determining the diagnosis even after conducting a thorough immunophenotypic assessment. Materials and methods: Histopathology slides of three cases of T-cell-rich CHL were retrieved and thoroughly examined to assess their clinical, immunomorphologic, and molecular features. Results: We present three cases containing cells that resembled lymphocyte predominant and Hodgkin Reed–Sternberg cells, expressing some B-cell antigens and CHL markers but all were lacking Epstein–Barr virus-encoded small RNA. All three cases were found in a background rich in T-cells with focal remaining follicular dendritic cell meshwork in one case. Only one case had few eosinophils while the other two had no background of eosinophils and plasma cells. Two patients presented with stage IIA and B-symptoms presented in one of them. Two patients were treated with four and six cycles of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine), respectively. One patient planned to be treated with four cycles of ABVD plus Rituximab therapy. Conclusions: Some cases of Reed–Sternberg cells can show expression of both B-cell and CHL markers. This overlapping characteristic, which has not been extensively discussed in the existing literature, presents a unique challenge for treatment. Further research into these neoplasms may reveal valuable diagnostic and therapeutic implications. Full article
(This article belongs to the Collection Lymphomas)
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<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number one. (<b>A</b>): Lymph nodes show total effacement of nodal architecture by vague nodules, and no remaining reactive lymphoid follicles detected (H&amp;E; 4×). (<b>B</b>): Reed–Sternberg (RS)-like cells present, no background of eosinophils nor plasma cells (H&amp;E; 40×). T-cells forming a ring around the neoplastic cells are observed (inset). (<b>C</b>): CD3 immunohistochemistry stain of T-cells in the background forming a rosette around the neoplastic cells (40×). (<b>D</b>): Target cells showing weak PAX5 nuclear expression compared to the background small non-neoplastic B-cells (40×).</p>
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<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number two. (<b>A</b>): Lymph nodes show partial nodal effacement of architecture by large vague nodules (H&amp;E; 4×). (<b>B</b>): Focal remaining reactive lymphoid follicles are detected at the periphery of the lymph node (H&amp;E; 40×). (<b>C</b>): CD4 immunohistochemistry stain of T-cells in the background forming a rosette around the target cells (40×). (<b>D</b>): Target cells showing CD30 membranous and Golgi positive expression (40×).</p>
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<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number three. (<b>A</b>): Disturbed normal lymphoid tissue by neoplastic Reed–Sternberg (RS)-like cells (very scattered), the background of eosinophils and rare plasma cells are seen (H&amp;E; 40×). (<b>B</b>): Target cells showing CD30 membranous and Golgi positive expression (10×). (<b>C</b>): Some of the target cells show dim PAX5 nuclear expression compared to the background small non-neoplastic B-cells (arrow) (40×). (<b>D</b>): Target cells showing strong CD20 membranous positive expression in a background rich in T-cells (40×).</p>
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8 pages, 801 KiB  
Communication
Secobutanolides Isolated from Lindera obtusiloba Stem and Their Anti-Inflammatory Activity
by Hye Jin Yang, Young-Sang Koh, MinKyun Na and Wei Li
Molecules 2024, 29(18), 4292; https://doi.org/10.3390/molecules29184292 - 10 Sep 2024
Viewed by 301
Abstract
In this study, a new secobutanolide, named secosubamolide B (3), along with three previously known secobutanolides (1, 2, and 4), were successfully isolated from a methanol extract of the stem of Lindera obtusiloba. The chemical structures [...] Read more.
In this study, a new secobutanolide, named secosubamolide B (3), along with three previously known secobutanolides (1, 2, and 4), were successfully isolated from a methanol extract of the stem of Lindera obtusiloba. The chemical structures of these compounds were elucidated through the analysis of spectroscopic data, and then compared with the existing literature to confirm their identities. Furthermore, the anti-inflammatory effect of these isolated compounds on bone marrow-derived dendritic cells stimulated by lipopolysaccharide (LPS) was evaluated. Compounds 13 showed the significant suppression of LPS-triggered IL-6 and IL-12 p40 production, with IC50 values between 1.8 and 24.1 µM. These findings may provide a scientific foundation for developing anti-inflammatory agents from L. obtusiloba. Full article
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<p>Structures of compounds <b>1</b>–<b>4</b> from the stem of <span class="html-italic">L. obtusiloba</span>.</p>
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<p>Effect of compounds <b>1</b>–<b>3</b> on the production of IL-12 p40 (<b>A</b>) and IL-6 (<b>B</b>) in LPS-stimulated BMDCs at concentrations of 1.0–50.0 μM. The results are shown as inhibition rates (%) relative to the levels observed in vehicle-treated DCs. Pos: SB203580. ND = Not Detected.</p>
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<p>Effect of compounds <b>1</b>–<b>3</b> on the production of IL-12 p40 (<b>A</b>) and IL-6 (<b>B</b>) in LPS-stimulated BMDCs at concentrations of 1.0–50.0 μM. The results are shown as inhibition rates (%) relative to the levels observed in vehicle-treated DCs. Pos: SB203580. ND = Not Detected.</p>
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27 pages, 30182 KiB  
Article
Synthetic Extracellular Matrix of Polyvinyl Alcohol Nanofibers for Three-Dimensional Cell Culture
by Thi Xuan Thuy Tran, Gyu-Min Sun, Hue Vy An Tran, Young Hun Jeong, Petr Slama, Young-Chae Chang, In-Jeong Lee and Jong-Young Kwak
J. Funct. Biomater. 2024, 15(9), 262; https://doi.org/10.3390/jfb15090262 - 10 Sep 2024
Viewed by 441
Abstract
An ideal extracellular matrix (ECM) replacement scaffold in a three-dimensional cell (3D) culture should induce in vivo-like interactions between the ECM and cultured cells. Highly hydrophilic polyvinyl alcohol (PVA) nanofibers disintegrate upon contact with water, resulting in the loss of their fibrous morphology [...] Read more.
An ideal extracellular matrix (ECM) replacement scaffold in a three-dimensional cell (3D) culture should induce in vivo-like interactions between the ECM and cultured cells. Highly hydrophilic polyvinyl alcohol (PVA) nanofibers disintegrate upon contact with water, resulting in the loss of their fibrous morphology in cell cultures. This can be resolved by using chemical crosslinkers and post-crosslinking. A crosslinked, water-stable, porous, and optically transparent PVA nanofibrous membrane (NM) supports the 3D growth of various cell types. The binding of cells attached to the porous PVA NM is low, resulting in the aggregation of cultured cells in prolonged cultures. PVA NMs containing integrin-binding peptides of fibronectin and laminin were produced to retain the blended peptides as cell-binding substrates. These peptide-blended PVA NMs promote peptide-specific cell adherence and growth. Various cells, including epithelial cells, cultured on these PVA NMs form layers instead of cell aggregates and spheroids, and their growth patterns are similar to those of the cells cultured on an ECM-coated PVA NM. The peptide-retained PVA NMs are non-stimulatory to dendritic cells cultured on the membranes. These peptide-retaining PVA NMs can be used as an ECM replacement matrix by providing in vivo-like interactions between the matrix and cultured cells. Full article
(This article belongs to the Special Issue Advanced Technologies for Processing Functional Biomaterials)
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<p>Production of water-stable and optically transparent PVA NMs. The membranes are untreated or soaked in distilled water and dried. (<b>a</b>) Structure of nanofibers measured by SEM. (<b>b</b>) Visual and optical transparency of membrane scanned using a spectrophotometer. (<b>c</b>) The structure and diameter of the PVA NMs measured by SEM.</p>
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<p>FTIR spectra of peptide-blended PVA NMs. Electrospun PVA NM is untreated (DW-treated PVA NM) or treated (HCl/DW-treated PVA NM) with HCl vapor for 2 min, followed by soaking in water for 18 h. The NMs are washed three times with PBS, dried, and then analyzed by FTIR spectroscopy. (<b>a</b>) FTIR spectra of HCl-treated PVA NM, (<b>b</b>) YIGSR-blended PVA NM, and (<b>c</b>) HCl and/or DW-treated YIGSR-PVA NMs.</p>
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<p>The 3D adhesion of cultured cells on PVA NMs. (<b>a</b>) CellTracker Red-labeled NIH 3T3 cells on PVA NM. (<b>b</b>) Cells alone without PVA NM imaging. (<b>c</b>) Cross-sectioned view of cells on PVA NM analyzed using a confocal microscope. Images are shown using the surface function of Imaris software. (<b>d</b>,<b>e</b>) NIH 3T3 and MLE-12 cells are cultured on HCl vapor-treated PVA NM and observed using SEM.</p>
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<p>Adhesion of cultured cells on PVA NMs. Merged fluorescence and DIC images of cells labeled with CellTracker Red and cultured for the indicated times on the membranes.</p>
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<p>Effects of NaOH treatment on peptide release and cell adhesion in peptide-blended PVA NMs. (<b>a</b>) Fluorescence levels in the media measured by fluorescence spectrometry and the membranes measured by confocal microscope. (<b>b</b>) Merged fluorescence and DIC images of fluorescence-labeled cells. (<b>c</b>) Images shown using the surface function of Imaris software and SEM. * <span class="html-italic">p</span> &lt; 0.05 versus 0.05 h treatment.</p>
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<p>Culture of cells on peptide-retained PVA NMs. (<b>a</b>) DIC images of the cells and CCK-8 assay of viable cells in culture media and attached to NMs. (<b>b</b>) CCK-8 assay of viable cells adhered to the membranes. (<b>c</b>) DIC and fluorescence images using a confocal microscope and CCK-8 assay of viable cells. * <span class="html-italic">p</span> &lt; 0.05 versus untreated. ** <span class="html-italic">p</span> &lt; 0.05 versus PVA NM. *** <span class="html-italic">p</span> &lt; 0.05 versus none.</p>
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<p>Culture of cells on PVA NMs containing various types of peptides. (<b>a</b>) NIH 3T3 and MLE-12 cells, (<b>b</b>) MLE-12 cells, and (<b>c</b>) primary colon epithelial cells observed using a confocal microscope and numbers of detached cells were counted based on 9 different areas (marked from 1 to 9). * <span class="html-italic">p</span> &lt; 0.05 versus without peptides. ** <span class="html-italic">p</span> &lt; 0.05 versus with one type of peptide. *** <span class="html-italic">p</span> &lt; 0.05 versus YIGSR. **** <span class="html-italic">p</span> &lt; 0.05 versus PVA NMs.</p>
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<p>Pattern of cell growth on ECM protein-coated and peptide-retained PVA NMs. (<b>a</b>) Imaris view of confocal microscopic images. The scale bar is 50 μm. (<b>b</b>) NIH 3T3 and (<b>c</b>) MLE-12 cells shown in confocal microscopic images, with cell nuclei (Hoechst 33342, blue), actin microfilaments (Alexa Flour 488 Phalloidin, green), zona occludin-1 (ZO-1, red) Arrows indicate ZO-1 expression.</p>
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<p>Growth rate of the cells cultured on the peptide-retained PVA NMs. (<b>a,b</b>) CCK-8 assay of NIH 3T3 cells. (<b>c</b>) NIH 3T3 cells cultured in the conditioned media on the culture plate. (<b>d</b>) CCK-8 assay of HepG2 cells. * <span class="html-italic">p</span> &lt; 0.05 versus culture plate, ** <span class="html-italic">p</span> &lt; 0.05 versus PVA NM.</p>
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<p>Culture of BMDCs on peptide-blended PVA NMs. (<b>a</b>) Cell morphology assessed using SEM. (<b>b</b>) Expression level of CD86 measured by flow cytometry. (<b>c</b>) The concentration of TNF-α measured by ELISA. (<b>d</b>) Cell morphology and CD86 expression evaluated by confocal microscopy (cell nuclei (Hoechst 33342, blue), actin microfilaments (Alexa Flour 488 Phalloidin, green), CD86 (red)).</p>
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18 pages, 5236 KiB  
Article
Dendritic Cells Pulsed with HAM/TSP Exosomes Sensitize CD4 T Cells to Enhance HTLV-1 Infection, Induce Helper T-Cell Polarization, and Decrease Cytotoxic T-Cell Response
by Julie Joseph, Thomas A. Premeaux, Ritesh Tandon, Edward L. Murphy, Roberta Bruhn, Christophe Nicot, Bobby Brooke Herrera, Alexander Lemenze, Reem Alatrash, Prince Baffour Tonto, Lishomwa C. Ndhlovu and Pooja Jain
Viruses 2024, 16(9), 1443; https://doi.org/10.3390/v16091443 - 10 Sep 2024
Viewed by 388
Abstract
HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive demyelinating disease of the spinal cord due to chronic inflammation. Hallmarks of disease pathology include dysfunctional anti-viral responses and the infiltration of HTLV-1-infected CD4+ T cells and HTLV-1-specific CD8+ T cells in the central nervous [...] Read more.
HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive demyelinating disease of the spinal cord due to chronic inflammation. Hallmarks of disease pathology include dysfunctional anti-viral responses and the infiltration of HTLV-1-infected CD4+ T cells and HTLV-1-specific CD8+ T cells in the central nervous system. HAM/TSP individuals exhibit CD4+ and CD8+ T cells with elevated co-expression of multiple inhibitory immune checkpoint proteins (ICPs), but ICP blockade strategies can only partially restore CD8+ T-cell effector function. Exosomes, small extracellular vesicles, can enhance the spread of viral infections and blunt anti-viral responses. Here, we evaluated the impact of exosomes isolated from HTLV-1-infected cells and HAM/TSP patient sera on dendritic cell (DC) and T-cell phenotypes and function. We observed that exosomes derived from HTLV-infected cell lines (OSP2) elicit proinflammatory cytokine responses in DCs, promote helper CD4+ T-cell polarization, and suppress CD8+ T-cell effector function. Furthermore, exosomes from individuals with HAM/TSP stimulate CD4+ T-cell polarization, marked by increased Th1 and regulatory T-cell differentiation. We conclude that exosomes in the setting of HAM/TSP are detrimental to DC and T-cell function and may contribute to the progression of pathology with HTLV-1 infection. Full article
(This article belongs to the Special Issue Human T-cell Leukemia Virus (HTLV) Infection and Treatment)
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Figure 1
<p>Exosomes activate and elicit proinflammatory responses in dendritic cells. (<b>A</b>) Representative phenotype of myeloid dendritic cell (mDC) and plasmacytoid dendritic cell (pDC) population differences with exosome stimulation (<span class="html-italic">n</span> = 4). GMFI levels of CD40, CD80, and CD86 in mDC and pDC populations untreated or stimulated with exosomes. Bars represent mean ± standard deviation (SD). (<b>B</b>) Expression of cytokines associated with Th1, Th2, Th17, and Treg polarization in total DCs stimulated with OSP2 cell-derived exosomes or LPS. Cytokines were grouped according to associated Th1 (IFN-γ, TNF-α, and IL-2), Th2 (IL-4, IL-12a, and IL-13), Th17 (IL-6 and IL-17a), and Treg (IL-10 and TGF-β) subsets. (<b>C</b>) Cytokine levels (pg/mL) in supernatants of mDCs (<b>left</b>) and pDCs (<b>right</b>) untreated or exosome-stimulated. Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>Dendritic cell-pulsed exosomes polarize T cells. (<b>A</b>) Exosome stimulation schematic and representative gating strategy. Donor CD3+ T cells and matched total DCs were isolated (<span class="html-italic">n</span> = 4). (<b>B</b>) DCs were exposed to OSP2-derived exosomes for 24 h and subsequently co-incubated with donor-matched T cells for another 24 h. Absolute count of CD4+ T-cells expressing subtype-associated markers: Th1 (IFN-γ, CCR5), Th2 (IL-4, CCR4), Th17 (IL-17, CCR6), and Treg (CD25, FoxP3). Quantification (pg/mL) of IFN-γ, TGF-β, and TNF-α cytokine levels in exosome-stimulated DC–T-cell co-culture (<span class="html-italic">n</span> = 3). (<b>C</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of percent positive CD4+ T cells expressing functional markers of IFN-γ, IL-4, IL-17a, CD25, CCR5, and CCR6 after stimulation with OSP2 cell-derived exosomes. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Dendritic cell-pulsed exosomes polarize T cells. (<b>A</b>) Exosome stimulation schematic and representative gating strategy. Donor CD3+ T cells and matched total DCs were isolated (<span class="html-italic">n</span> = 4). (<b>B</b>) DCs were exposed to OSP2-derived exosomes for 24 h and subsequently co-incubated with donor-matched T cells for another 24 h. Absolute count of CD4+ T-cells expressing subtype-associated markers: Th1 (IFN-γ, CCR5), Th2 (IL-4, CCR4), Th17 (IL-17, CCR6), and Treg (CD25, FoxP3). Quantification (pg/mL) of IFN-γ, TGF-β, and TNF-α cytokine levels in exosome-stimulated DC–T-cell co-culture (<span class="html-italic">n</span> = 3). (<b>C</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of percent positive CD4+ T cells expressing functional markers of IFN-γ, IL-4, IL-17a, CD25, CCR5, and CCR6 after stimulation with OSP2 cell-derived exosomes. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Immune checkpoint and anti-viral cytokine expression of exosomes from individuals with HAM/TSP. (<b>A</b>) Representative NTA of exosomes isolated from asymptomatic carrier (AC, <span class="html-italic">n</span> = 1) and HAM/TSP (HAM, <span class="html-italic">n</span> = 3) patient sera in individuals infected with HTLV-1 or HTLV-2. (<b>B</b>) Quantification of immune checkpoint proteins BTLA, LAG-3, PD-1, and PD-L2 in exosomes from AC and HAM/TSP patient sera from individuals infected with HTLV-1, left, or HTLV-2, right. (<b>C</b>) Quantification of IFN-γ and TNF-α levels in exosomes from AC and HAM/TSP patient sera from individuals infected with HTLV-1, left, or HTLV-2, right. Bars represent mean ± standard deviation (SD). Statistical differences were determined by unpaired two-tailed T-tests of technical replicates, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>HAM/TSP exosomes skew Th1/Treg responses and sensitize cells toward infection. (<b>A</b>) Exosome stimulation schematic and representative gating strategy. CD3+ T cells and total DCs were isolated from matched donors (<span class="html-italic">n</span> = 4). DCs were stimulated with exosomes from HTLV-1 patient sera for 24 h and subsequently co-incubated with donor-matched T cells for another 24 h. Differences in GMFI of representative markers are plotted. Bottom, absolute count of CD4+ T cells expressing subtype-associated markers: Th1 (IFN-y and CCR5), Th2 (IL-4 and CCR4), Th17 (IL-17 and CCR6), and Treg (CD25 and FoxP3). (<b>B</b>) Quantification (pg/mL) of IFN-γ, TGF-β, and TNF-α cytokine levels in patient exosome-stimulated DC–T-cell co-culture. (<b>C</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of percent positive CD4+ T cells expressing functional markers of IFN-γ, IL-4, IL-17a, CD25, CCR5, and CCR6 after stimulation with HTLV-1 patient-derived exosomes. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4 Cont.
<p>HAM/TSP exosomes skew Th1/Treg responses and sensitize cells toward infection. (<b>A</b>) Exosome stimulation schematic and representative gating strategy. CD3+ T cells and total DCs were isolated from matched donors (<span class="html-italic">n</span> = 4). DCs were stimulated with exosomes from HTLV-1 patient sera for 24 h and subsequently co-incubated with donor-matched T cells for another 24 h. Differences in GMFI of representative markers are plotted. Bottom, absolute count of CD4+ T cells expressing subtype-associated markers: Th1 (IFN-y and CCR5), Th2 (IL-4 and CCR4), Th17 (IL-17 and CCR6), and Treg (CD25 and FoxP3). (<b>B</b>) Quantification (pg/mL) of IFN-γ, TGF-β, and TNF-α cytokine levels in patient exosome-stimulated DC–T-cell co-culture. (<b>C</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of percent positive CD4+ T cells expressing functional markers of IFN-γ, IL-4, IL-17a, CD25, CCR5, and CCR6 after stimulation with HTLV-1 patient-derived exosomes. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>HAM/TSP exosomes skew T cells to Th1/Treg profiles in patient PBMCs. (<b>A</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of the polarization of T cells from individuals with HTLV-1 with or without HTLV-1 sera-derived exosomes. (<b>B</b>) Representative flow plots and quantification (<span class="html-italic">n</span> = 4) of HTLV-1-infected patient CD4+ T cells with or without HTLV-1 sera-derived exosome stimulation. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Exosomes diminish CD8+ T-cell activity. (<b>A</b>) Schematic of treatment and gating strategy of CD8+ T cells. CD3+ T cells and total DCs were isolated from matched donors (<span class="html-italic">n</span> = 4). DCs were stimulated with OSP2-derived exosomes for 24 h and subsequently co-incubated with donor-matched T cells for another 24 h. Absolute counts of CD8+ T cells expressing MIP-1a, Granzyme B, Perforin, IFN-γ, PD-1, and Ki67 after co-incubation with exosome-pulsed DCs. (<b>B</b>) Absolute counts of CD8+ T cells after CD3+/CD28+ activation and stimulation with OSP2 exosomes alone, or exosomes incubated with a cocktail of anti-PD-L2, anti-BTLA, anti-LAG-3, and anti-PD-1 blocking antibodies. (<b>C</b>) Absolute counts of CD8+ T cells expressing MIP-1a, Granzyme B, Perforin, IFN-γ, PD-1, and Ki67 in HTLV-1-infected patient T cells (<span class="html-italic">n</span> = 4) stimulated with patient sera exosomes alone or exosomes incubated with a cocktail of anti-PD-L2, anti-BTLA, anti-LAG-3, and anti-PD-1 blocking antibodies. Counts were determined by experiments with <span class="html-italic">n</span> = 3/4 healthy donors, and error bars represent SEM of donor variation. Bars represent mean ± standard deviation (SD). Statistical differences were determined by one-way ANOVA. (<b>D</b>) Western blot and densitometry of CD3/CD28-stimulated T cells treated with Jurkat and OSP2 exosomes alone, or exosomes incubated with a cocktail of anti-PD-L2, anti-BTLA, anti-LAG-3, and anti-PD-1 blocking antibodies. Blot was probed for phosphorylated AKT or ERK. Error bars represent SEM of blot variation; statistical differences were determined by paired two-tailed T-test, * <span class="html-italic">p</span> &lt; 0.05.</p>
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19 pages, 6886 KiB  
Article
GSK-3β in Dendritic Cells Exerts Opposite Functions in Regulating Cross-Priming and Memory CD8 T Cell Responses Independent of β-Catenin
by Chunmei Fu, Jie Wang, Tianle Ma, Congcong Yin, Li Zhou, Björn E. Clausen, Qing-Sheng Mi and Aimin Jiang
Vaccines 2024, 12(9), 1037; https://doi.org/10.3390/vaccines12091037 - 10 Sep 2024
Viewed by 557
Abstract
GSK-3β plays a critical role in regulating the Wnt/β-catenin signaling pathway, and manipulating GSK-3β in dendritic cells (DCs) has been shown to improve the antitumor efficacy of DC vaccines. Since the inhibition of GSK-3β leads to the activation of β-catenin, we hypothesize that [...] Read more.
GSK-3β plays a critical role in regulating the Wnt/β-catenin signaling pathway, and manipulating GSK-3β in dendritic cells (DCs) has been shown to improve the antitumor efficacy of DC vaccines. Since the inhibition of GSK-3β leads to the activation of β-catenin, we hypothesize that blocking GSK-3β in DCs negatively regulates DC-mediated CD8 T cell immunity and antitumor immunity. Using CD11c-GSK-3β−/− conditional knockout mice in which GSK-3β is genetically deleted in CD11c-expressing DCs, we surprisingly found that the deletion of GSK-3β in DCs resulted in increased antitumor immunity, which contradicted our initial expectation of reduced antitumor immunity due to the presumed upregulation of β-catenin in DCs. Indeed, we found by both Western blot and flow cytometry that the deletion of GSK-3β in DCs did not lead to augmented expression of β-catenin protein, suggesting that GSK-3β exerts its function independent of β-catenin. Supporting this notion, our single-cell RNA sequencing (scRNA-seq) analysis revealed that GSK-3β-deficient DCs exhibited distinct gene expression patterns with minimally overlapping differentially expressed genes (DEGs) compared to DCs with activated β-catenin. This suggests that the deletion of GSK-3β in DCs is unlikely to lead to upregulation of β-catenin at the transcriptional level. Consistent with enhanced antitumor immunity, we also found that CD11c-GSK-3β−/− mice exhibited significantly augmented cross-priming of antigen-specific CD8 T cells following DC-targeted vaccines. We further found that the deletion of GSK-3β in DCs completely abrogated memory CD8 T cell responses, suggesting that GSK-3β in DCs also plays a negative role in regulating the differentiation and/or maintenance of memory CD8 T cells. scRNA-seq analysis further revealed that although the deletion of GSK-3β in DCs positively regulated transcriptional programs for effector differentiation and function of primed antigen-specific CD8 T cells in CD11c-GSK-3β−/− mice during the priming phase, it resulted in significantly reduced antigen-specific memory CD8 T cells, consistent with diminished memory responses. Taken together, our data demonstrate that GSK-3β in DCs has opposite functions in regulating cross-priming and memory CD8 T cell responses, and GSK-3β exerts its functions independent of its regulation of β-catenin. These novel insights suggest that targeting GSK-3β in cancer immunotherapies must consider its dual role in CD8 T cell responses. Full article
(This article belongs to the Special Issue Vaccines Targeting Dendritic Cells)
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Figure 1
<p>Deletion of GSK-3β in DCs led to augmented antitumor immunity in CD11c-GSK-3β<sup>−/−</sup> mice. WT and CD11c-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 7–9) were inoculated with B16F10 melanoma cells, and tumor sizes were monitored. (<b>A</b>,<b>B</b>) CD11c-GSK-3β<sup>−/−</sup> mice exhibited reduced tumor growth compared to WT mice. Tumor sizes from the day of treatment are shown in (<b>A</b>) and tumor weights at the end of the experiment (day 20) are shown in (<b>B</b>). A linear mixed model (Lme4) was fitted to the data in (<b>A</b>), and ANOVA for the fitted linear mixed model was then performed to determine the difference between groups. Student’s <span class="html-italic">t</span>-tests were used for (<b>B</b>). *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Photo of the tumors at the day 20 after tumor inoculation. Data are representative of two experiments.</p>
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<p>GSK-3β<sup>−/−</sup> DCs exhibited different expression profiles from β-catenin<sup>active</sup> DCs by scRNA-seq. DCs sorted from spleens of WT (GSK-3β<sup>Flox/Flox</sup>) and CD11c-GSK-3β<sup>−/−</sup> mice, or from WT (β-catenin <sup>Exon3/Exon3</sup>) and CD11c-β-catenin<sup>active</sup> (CD11c-Cre β-catenin<sup>Exon3/Exon3</sup>), were subjected to scRNA-seq as described. (<b>A</b>) Uniform manifold approximation and projection (UMAP) dimensionality reduction mapping analysis of single-cell gene expression of integrated WT (GSK-3β<sup>Flox/Flox</sup>) and GSK-3β<sup>−/−</sup> DCs, and WT (β-catenin<sup>Exon3/Exon3</sup>) and β-catenin<sup>active</sup> DCs. Each dot represents one single cell. A total of 13 clusters were identified and color-coded as indicated. (<b>B</b>) Bubble plots showing the expression of key markers for pDC, cDC1, cDC2, and MoDCs cells among 13 UMAP clusters. The sizes of dots represent the percentages expressed; the color of dot represents the average expression. (<b>C</b>) Bubble plots depicting expression of top DEGs for UMAP clusters shown in (<b>A</b>). (<b>D</b>) Distribution of cells from WT/GSK-3β<sup>Flox/Flox</sup> and GSK-3β<sup>−/−</sup> (left), or WT/β-catenin<sup>Exon3/Exon3</sup> and β-catenin<sup>active</sup> DCs (right) within each of the 13 clusters as depicted in (<b>A</b>). (<b>E</b>) Venn plot showing the overlap of downregulated DEGs (left) and upregulated DEGs (right) in GSK-3β<sup>−/−</sup> DCs versus WT/GSK-3β<sup>Flox/Flox</sup> DCs (GSK-3β<sup>−/−</sup> vs. WT), and β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs (β-catenin<sup>active</sup> vs. WT). (<b>F</b>) Volcano plot visualizing expression of DEGs in GSK-3β<sup>−/−</sup> and WT/GSK-3β<sup>Flox/Flox</sup> DCs, and their expression pattern in β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs. DEGs in GSK-3β<sup>−/−</sup> DCs versus WT/GSK-3β<sup>Flox/Flox</sup> DCs are shown in volcano plot (upper), and expression of downregulated DEGs (lower left) and upregulated DEGs (lower right) in β-catenin<sup>active</sup> and WT/β-catenin<sup>Exon3/Exon3</sup> DCs are analyzed and shown in volcano plots. (<b>G</b>) GO enrichment analysis identifies top regulated biological process pathways in in GSK-3β<sup>−/−</sup> DCs vs. WT/GSK-3β<sup>Flox/Flox</sup> DCs (upper), and β-catenin<sup>active</sup> vs. WT/β-catenin<sup>Exon3/Exon3</sup> DCs (lower).</p>
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<p>Deletion of GSK-3β in DCs does not upregulate β-catenin. (<b>A</b>,<b>B</b>) GSK-3β<sup>−/−</sup> cDCs express similar levels of β-catenin to WT cDCs. WT and GSK-3β<sup>−/−</sup> splenic cDCs were isolated and subjected to Western blot. (<b>A</b>) Expression of GSK-3α/β, β-catenin, and β-actin by Western blotting is shown. One of three experiments is shown. (<b>B</b>) Statistical analysis of β-catenin expression is shown. The relative expression of β-catenin Western blot intensity relative to that of b-actin loading control was calculated, and the ratios for WT cDCs for each experiment were set at 1.0. (<b>C</b>,<b>D</b>) Deletion of GSK-3β in DCs does not upregulate β-catenin. Histogram overlay of β-catenin expression (<b>C</b>) and mean fluorescence intensity (MFI) of β-catenin expression (<b>D</b>) on gated CD11c<sup>+</sup>Bst2<sup>−</sup> cDCs are shown. Student’s <span class="html-italic">t</span>-test, and NS &gt; 0.05. Data shown are representative of at least three experiments.</p>
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<p>Deletion of GSK-3β in DCs abrogated memory CD8 T cell responses despite augmented cross-priming. (<b>A</b>,<b>B</b>) Deletion of GSK-3β in DCs led to significantly augmented cross-priming. WT and DC-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 4) were immunized with anti-DEC-205-OVA with CpG following adoptive transfer of naïve CFSE-labeled Thy1.1<sup>+</sup> OTI cells, and cross-priming was examined at day 4 after immunization. (<b>A</b>) The percentages of Thy1.1<sup>+</sup> OTI cells out of total CD8 T cells, and (<b>B</b>) the percentages of IFN-γ<sup>+</sup> cells out of total Thy1.1<sup>+</sup>CD8<sup>+</sup> OTI cells in both spleen and draining LN are shown. (<b>C</b>) CD8 memory responses were abrogated in CD11c-GSK-3β<sup>−/−</sup> mice upon recall. Immunized WT and CD11c-GSK-3β<sup>−/−</sup> mice (<span class="html-italic">n</span> = 4–5) were recalled at day 21 and examined 5 days later. The percentages of Thy1.1<sup>+</sup> OTI cells out of total CD8 T cells are shown. Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001. Data shown are representative of at least two experiments.</p>
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<p>scRNA-seq of OVA-specific CD8 T cells identifies distinct populations and reveals differences between CD8 T cells primed in WT and CD11c-GSK-3β<sup>−/−</sup> mice. WT and CD11c-GSK-3β<sup>−/−</sup> mice adoptively transferred Thy1.1<sup>+</sup> OTI CD8 T cells were immunized with anti-DEC-205-OVA plus CpG. Spleen cells were harvested at day 4 or day 10 after immunization, and FACS-sorted OTI cells were subjected to scRNA-seq as described. (<b>A</b>,<b>B</b>) UMAP-dimensionality reduction mapping analysis of single-cell gene expression data of OTI cells isolated 4 or 10 days following vaccination with ant-DEC-205-OVA. Each dot represents one single cell. A total of 9 clusters were identified and color-coded as indicated. UMAP visualization of single cells from combined OTI cells (<b>A</b>), or OT1 cells from WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 and day 10 (<b>B</b>) are shown. (<b>C</b>) Bubble plots depicting expression of top DEGs for UMAP clusters shown in (<b>A</b>). (<b>D</b>) Distribution of OTI cells from either WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 or day 10 within each of the 9 clusters as depicted in (<b>A</b>). (<b>E</b>) Bubble plots showing the key signatures for CD8 T cells effector and memory phenotype. (<b>F</b>) Expression of effector markers among the UMAP clusters. Gradient expression levels are color-coded as indicated. (<b>G</b>) Violin plot depicting the module score of gene sets associated with effector on OTI cells from either WT or CD11c-GSK-3β<sup>−/−</sup> mice at day 4 or day 10. *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001 (<b>H</b>) Signaling pathways that are significantly downregulated or upregulated in OTI cells primed in CD11c-GSK-3β<sup>−/−</sup> mice compared to OTI cells from WT mice at day 4 and day 10.</p>
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<p>Schematic representation of GSK-3β’s dual roles in regulating CD8 T cell responses. Inhibition of GSK-3β is generally believed to upregulate β-catenin, leading to increased IL-10 production, which suppresses cross-priming and reduces memory CD8 T cell responses. However, our studies demonstrate that genetic deletion of GSK-3β in CD11c<sup>+</sup> DCs does not result in β-catenin accumulation (activation). Instead, the deletion of GSK-3β in DCs enhances cross-priming of CD8 T cells, as indicated by an increase in effector cells and a higher effector index, based on scRNA-seq analysis. Despite this enhanced cross-priming, memory CD8 T cells are nearly abrogated in CD11c-GSK-3β<sup>−/−</sup> mice, likely due to a significant loss of both effector and memory CD8 T cell populations. Collectively, these findings reveal novel mechanisms by which GSK-3β exerts opposing effects on CD8 T cell responses.</p>
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12 pages, 517 KiB  
Review
Recent Advances in the Study of Alphaherpesvirus Latency and Reactivation: Novel Guidance for the Design of Herpesvirus Live Vector Vaccines
by Shinuo Cao, Mo Zhou, Shengwei Ji, Dongxue Ma and Shanyuan Zhu
Pathogens 2024, 13(9), 779; https://doi.org/10.3390/pathogens13090779 - 10 Sep 2024
Viewed by 375
Abstract
Alphaherpesviruses, including herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), and varicella-zoster virus (VZV), infect a diverse array of hosts, spanning both humans and animals. Alphaherpesviruses have developed a well-adapted relationship with their hosts through long-term evolution. Some alphaherpesviruses [...] Read more.
Alphaherpesviruses, including herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), and varicella-zoster virus (VZV), infect a diverse array of hosts, spanning both humans and animals. Alphaherpesviruses have developed a well-adapted relationship with their hosts through long-term evolution. Some alphaherpesviruses exhibit a typical neurotropic characteristic, which has garnered widespread attention and in-depth research. Virus latency involves the retention of viral genomes without producing infectious viruses. However, under stress, this can be reversed, resulting in lytic infection. Such reactivation events can lead to recurrent infections, manifesting as diseases like herpes labialis, genital herpes, and herpes zoster. Reactivation is a complex process influenced by both viral and host factors, and identifying how latency and reactivation work is vital to developing new antiviral therapies. Recent research highlights a complex interaction among the virus, neurons, and the immune system in regulating alphaherpesvirus latency and reactivation. Neurotropic alphaherpesviruses can breach host barriers to infect neurons, proliferate extensively within their cell bodies, and establish latent infections or spread further. Whether infecting neurons or spreading further, the virus undergoes transmission along axons or dendrites, making this process an indispensable part of the viral life cycle and a critical factor influencing the virus’s invasion of the nervous system. Research on the transmission process of neurotropic alphaherpesviruses within neurons can not only deepen our understanding of the virus but can also facilitate the targeted development of corresponding vaccines. This review concentrates on the relationship between the transmission, latency, and activation of alphaherpesviruses within neurons, summarizes recent advancements in the field, and discusses how these findings can inform the design of live virus vaccines for alphaherpesviruses. Full article
(This article belongs to the Special Issue Herpesvirus Latency and Reactivation)
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<p>Directional spread of alphaherpesvirus entering the mammalian nervous system.</p>
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22 pages, 14482 KiB  
Article
Key Disease-Related Genes and Immune Cell Infiltration Landscape in Inflammatory Bowel Disease: A Bioinformatics Investigation
by Kawthar S. Alghamdi, Rahaf H. Kassar, Wesam F. Farrash, Ahmad A. Obaid, Shakir Idris, Alaa Siddig, Afnan M. Shakoori, Sallwa M. Alshehre, Faisal Minshawi and Abdulrahman Mujalli
Int. J. Mol. Sci. 2024, 25(17), 9751; https://doi.org/10.3390/ijms25179751 - 9 Sep 2024
Viewed by 474
Abstract
Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn’s disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed [...] Read more.
Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn’s disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein–protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics. Full article
(This article belongs to the Special Issue Immunoanalytical and Bioinformatics Methods in Immunology Research)
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<p>Differential expression analysis of Crohn’s disease (CD). (<b>a</b>) Volcano plots across four CD datasets. The colored dots represent the significant DEGs identified at |log2FC| ≥ 1 and an adjusted <span class="html-italic">p</span>-value of ≤0.05. The top 10 genes within each dataset are shown. (<b>b</b>) The upset plots depict DEG distribution across CD datasets. (<b>c</b>) Heatmap of top 10 DEGs identified by meta-analysis. The expression heatmap depicts expression levels of significantly different upregulated and downregulated genes. The color indicates high expression (red) and low expression (green).</p>
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<p>Differential expression analysis of ulcerative colitis (UC). (<b>a</b>) Volcano plots across four UC datasets. The colored dots represent the significant DEGs identified at |log2FC| ≥ 1 and an adjusted <span class="html-italic">p</span>-value of ≤0.05. The top 10 genes within each dataset are shown. (<b>b</b>) The upset plots depict DEG distribution across UC datasets. (<b>c</b>) Heatmap of top 10 DEGs identified by meta-analysis. The expression heatmap depicts expression levels of significantly different upregulated and downregulated genes. The color indicates high expression (red) and low expression (green).</p>
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<p>Enrichment analysis of DEGs. Bubble plot of top 20 gene ontology (GO) and KEGG signaling pathways for (<b>a</b>) CD and (<b>b</b>) UC. The bubble color scaled the enrichment score. The size of the bubbles represents the level of DEG enrichment within each pathway. (<b>c</b>) The Sankey plot represents both shared and distinct pathways between CD and UC.</p>
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<p>Characterization of IBD-DEGs. (<b>a</b>) Venn diagram illustrates shared DEGs between CD and UC, identifying 180 IBD-DEGs with 106 upregulated and 74 downregulated. (<b>b</b>) Tissue specificity enrichment analysis of IBD-DEGs shows predominant enrichment in tissues such as the small intestine, appendix, colon, duodenum, and rectum. (<b>c</b>) PPI network of IBD-DEGs, displaying top genes with their centrality parameters obtained from network analysis. Node size corresponds to the degree of connectivity. (<b>d</b>) Hub genes identified from the PPI network were ranked based on their degree of connectivity.</p>
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<p>Potential key IB-DEGs. (<b>a</b>) PPI network with clusters identified using MCODE and BiCoN clustering methods in Cytoscape. Nodes with lavender color indicate the genes in the clusters and the cluster regions in the PPI network (<b>b</b>) 14 key IBD-DEGs identified through the intersection of clusters identified by both methods. (<b>c</b>) Heatmap showing the validation of the candidate IBD-DEGs across eight external validation datasets (GSE117993, GSE4183, GSE13367, GSE16879, GSE36807, GSE38713, GSE6731, and GSE59071). Consistent expression patterns of the identified genes were observed across these datasets. Red signifies upregulation, and blue signifies downregulation of expression levels.</p>
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<p>Disease modules of key IBD-DEGs and mechanistic pathways. (<b>a</b>) An illustration of IBD-related genes recovered through the Get Disease Genes function in NeDRex platform. (<b>b</b>) Disease module obtained from IBD-related genes and potential key IBD genes using DIAMOnD algorithm. Nine out of 14 key IBD-DEGs are present in the subnetwork highlighted in yellow. (<b>c</b>) Enriched pathways for key IBD-DEGs.</p>
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<p>Immune cell infiltration fraction between inflamed and non-inflamed IBD tissues. Violin plots of the proportion of 22 immune cells in inflamed (<span class="html-italic">n</span> = 397, red) vs. non-inflamed (<span class="html-italic">n</span> = 370, blue) IBD tissues. The red boxplot represents inflamed, and the blue boxplot represents non-inflamed tissues. Significance levels are indicated as follows: ns = non-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>Workflow used in this study.</p>
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15 pages, 1387 KiB  
Review
Transplant Immunology in Liver Transplant, Rejection, and Tolerance
by Masaya Yokoyama, Daisuke Imai, Samuel Wolfe, Ligee George, Yuzuru Sambommatsu, Aamir A. Khan, Seung Duk Lee, Muhammad I. Saeed, Amit Sharma, Vinay Kumaran, Adrian H. Cotterell, Marlon F. Levy and David A. Bruno
Livers 2024, 4(3), 420-434; https://doi.org/10.3390/livers4030031 - 9 Sep 2024
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Abstract
Liver transplantation is the most effective treatment for end-stage liver disease. Despite improvements in surgical techniques, transplant rejection remains a significant concern. The liver is considered an immune-privileged organ due to its unique microenvironment and complex interactions among various cell types. Alloimmune responses [...] Read more.
Liver transplantation is the most effective treatment for end-stage liver disease. Despite improvements in surgical techniques, transplant rejection remains a significant concern. The liver is considered an immune-privileged organ due to its unique microenvironment and complex interactions among various cell types. Alloimmune responses mediated by T cells and antigen-presenting cells (APCs) play crucial roles in transplant rejection. The liver’s dual blood supply and unique composition of its sinusoidal endothelial cells (LSECs), Kupffer cells (KCs), hepatocytes, and hepatic stellate cells (HSCs) contribute to its immune privilege. Alloantigen recognition by T cells occurs through direct, indirect, and semidirect pathways, leading to acute cellular rejection (ACR) and chronic rejection. ACR is a T cell-mediated process that typically occurs within the first few weeks to months after transplantation. Chronic rejection, on the other hand, is a gradual process characterized by progressive fibrosis and graft dysfunction, often leading to graft loss. Acute antibody-mediated rejection (AMR) is less common following surgery compared to other solid organ transplants due to the liver’s unique anatomy and immune privilege. However, when it does occur, AMR can be aggressive and lead to rapid graft dysfunction. Despite improvements in immunosuppression, rejection remains a challenge, particularly chronic rejection. Understanding the mechanisms of rejection and immune tolerance, including the roles of regulatory T cells (Tregs) and hepatic dendritic cells (DCs), is crucial for improving transplant outcomes. Strategies to induce immune tolerance, such as modulating DC function or promoting Treg activity, hold promise for reducing rejection and improving long-term graft survival. This review focuses on the liver’s unique predisposition to rejection and tolerance, highlighting the roles of individual cell types in these processes. Continued research into the mechanisms of alloimmune responses and immune tolerance in liver transplantation is essential for developing more effective therapies and improving long-term outcomes for patients with end-stage liver disease. Full article
(This article belongs to the Special Issue The Liver as the Center of the Internal Defence System of the Body)
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<p>Immunological basis of T cell-mediated rejection.</p>
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<p>Recognition of alloantigen presentation.</p>
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<p>Pathways of antibody-mediated rejection.</p>
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18 pages, 1603 KiB  
Article
Impaired Periodontitis-Induced Cytokine Production by Peripheral Blood Monocytes and Myeloid Dendritic Cells in Patients with Rheumatoid Arthritis: A Case–Control Study
by Daniela S. Silva, Paula Laranjeira, Ana Silva, Isabel Silva, Marta Kaminska, Piotr Mydel, Charlotte de Vries, Karin Lundberg, José António P. da Silva, Isabel P. Baptista and Artur Paiva
J. Clin. Med. 2024, 13(17), 5297; https://doi.org/10.3390/jcm13175297 - 6 Sep 2024
Viewed by 666
Abstract
Background: Immune cells from rheumatoid arthritis (RA) patients display a reduced in vitro response to Porphyromonas gingivalis (P. gingivalis), which may have functional immune consequences. The aim of this study was to characterize, by flow cytometry, the frequency/activity of monocytes [...] Read more.
Background: Immune cells from rheumatoid arthritis (RA) patients display a reduced in vitro response to Porphyromonas gingivalis (P. gingivalis), which may have functional immune consequences. The aim of this study was to characterize, by flow cytometry, the frequency/activity of monocytes and naturally occurring myeloid dendritic cells (mDCs) in peripheral blood samples from patients with periodontitis and patients with periodontitis and RA. Methods: The relative frequency of monocytes and mDCs in the whole blood, the frequency of these cells producing TNFα or IL-6 and the protein expression levels for each cytokine, before and after stimulation with lipopolysaccharide (LPS) from Escherichia coli plus interferon-γ (IFN-γ), were assessed by flow cytometry, in peripheral blood samples from 10 healthy individuals (HEALTHY), 10 patients with periodontitis (PERIO) and 17 patients with periodontitis and RA (PERIO+RA). Results: The frequency of monocytes and mDCs producing IL-6 or TNF-α and the expression of IL-6 and TNF-α in the PERIO group were generally higher. Within the PERIO+RA group, P. gingivalis and related antibodies were negatively correlated with the monocyte and mDC expression of IL-6. A subgroup of the PERIO+RA patients that displayed statistically significantly lower frequencies of monocytes producing IL-6 after activation presented statistically significantly higher peptidylarginine deiminase (PAD)2/4 activity, anti-arg-gingipain (RgpB) IgG levels, mean probing depth (PD), periodontal inflamed surface area (PISA) and bleeding on probing (BoP). Conclusions: In the patients with PERIO+RA, innate immune cells seemed to produce lower amounts of pro-inflammatory cytokines, which are correlated with worse periodontitis-related clinical and microbiological parameters. Full article
(This article belongs to the Section Immunology)
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<p>Gating strategy to identify peripheral blood monocytes and myeloid dendritic cells (mDCs). Monocytes (blue events) were identified based on their typical FSC/SSC characteristics (<b>A</b>), which lay between neutrophils (yellow events) and lymphocytes (identified in orange), together with high expression of CD45 (<b>B</b>), CD33 (<b>C</b>), HLA-DR (<b>D</b>) and CD14 (<b>E</b>,<b>F</b>). The mDCs (pink events) presented FSC/SSC values between those of monocytes and lymphocytes (<b>A</b>), along with lower levels of CD45 (<b>B</b>), and higher levels of CD33 (<b>C</b>) and HLA-DR (<b>D</b>), compared to monocytes; mDCs do not express CD14 (<b>E</b>,<b>F</b>). The percentages of monocytes and mDCs producing TNF-α (<b>E</b>) or IL-6 (<b>F</b>) were also evaluated. Light pink events correspond to eosinophils and gray events correspond to the remaining (non-identified) peripheral blood cells.</p>
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<p>Graphical representation of the correlation matrix of the frequency and function of IL-6 and monocyte and dendritic cell producers of TNF-α and PAD2/4 activity, anti-RgpB IgG, <span class="html-italic">P. gingivalis</span> CFU, mean PD and mean CAL. The figure shows correlation coefficients r (scale on the right); p-values are shown as asterisks (* &lt; 0.05; ** &lt; 0.01; *** &lt; 0.001). CAL = clinical attachment loss; CFU = colony-forming units; IL-6 = interleukin-6; mDCs = myeloid dendritic cells; MFI = mean fluorescence intensity of positive cells; PAD = peptidylarginine-deiminase; PD = probing depth; <span class="html-italic">P. gingivalis</span> = <span class="html-italic">Porphyromonas gingivalis</span>; RgpB = arginine-specific gingipain; TNF-α = tumor necrosis factor-α; % = percentage of positive cells.</p>
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