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17 pages, 2681 KiB  
Article
Onvansertib and Navitoclax Combination as a New Therapeutic Option for Mucinous Ovarian Carcinoma
by Serena Petrella, Marika Colombo, Mirko Marabese, Chiara Grasselli, Andrea Panfili, Michela Chiappa, Valentina Sancisi, Ilaria Craparotta, Maria C. Barbera, Giada A. Cassanmagnago, Marco Bolis and Giovanna Damia
Int. J. Mol. Sci. 2025, 26(2), 472; https://doi.org/10.3390/ijms26020472 (registering DOI) - 8 Jan 2025
Abstract
Mucinous epithelial ovarian cancer (mEOC) is a rare subtype of epithelial ovarian cancer, characterized by poor responses to standard platinum-based chemotherapy. Polo-like kinase 1 (PLK1) is a key regulator of mitosis and cell cycle progression and its inhibition has been recently identified as [...] Read more.
Mucinous epithelial ovarian cancer (mEOC) is a rare subtype of epithelial ovarian cancer, characterized by poor responses to standard platinum-based chemotherapy. Polo-like kinase 1 (PLK1) is a key regulator of mitosis and cell cycle progression and its inhibition has been recently identified as a target in mEOC. In this study, we aimed to identify further therapeutic targets in mEOC using a CRISPR/Cas9 library targeting 3015 genes, with and without treatment with onvansertib, a PLK1 inhibitor. We identified twelve genes associated with cell survival (ZC2HC1C, RPA2, KIN17, TUBG1, SMC2, CDC26, CDC42, HOXA9, TAF10, SENP1, MRPS31, and COPS2) and three genes (JUND, CARD9, and BCL2L2) in synthetic lethality with onvansertib treatment. We validated that SENP1 downregulation is important for the growth of mEOC cells through esiRNA interference and the use of a pharmacological inhibitor Momordin Ic. The downregulation of CARD9 and BCL2L2 combined with subtoxic doses of onvansertib interfered with mEOC cell growth. Interestingly, the combination of navitoclax, an inhibitor of BcL2 family members including BCL2L2, was synergistic in all four of the mEOC cell lines tested and substantially induced cell death through apoptosis. These data support the use of a combination of navitoclax and onvansertib as a new therapeutic strategy for mEOC. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1

Figure 1
<p>Screening experiment and Cas9 in mEOC cell lines. (<b>A</b>) Western blot analysis of Cas9 in mEOC cells. (<b>B</b>–<b>D</b>) Flow cytometric analyses of GFP positive (GFP+) and negative (GFP−) cells after lentivirus transduction of GFP plasmid carrying gRNA for GFP in MCAS/Cas9 (<b>B</b>), EFO27/Cas9 (<b>C</b>) and TOV2414/Cas9 (<b>D</b>). (<b>E</b>) Workflow of screening experiment.</p>
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<p>Genes identified by CRISPR/Cas9 screening in EFO27/Cas9. (<b>A</b>) There were 12 genes with an FDR &lt; 0.05 compared to the not treated groups T1 and T0. These are the genes that, if depleted, lead to cell death. (<b>B</b>) Comparing T1-treated with T1-untreated genes, the selection based on the FDR &lt; 0.1 of individual sgRNAs allowed for the identification of three genes with potential synthetic lethality with onvansertib. For each indicated gene, blue lines represent the gRNAs that were downregulated, while the red lines are the gRNAs that were upregulated. CRISPR-seq data were processed using the MAGeCK pipeline [<a href="#B36-ijms-26-00472" class="html-bibr">36</a>] in the MAGeCKFlute R package [<a href="#B37-ijms-26-00472" class="html-bibr">37</a>].</p>
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<p>Effects of esiRNA <span class="html-italic">KIN17</span> and <span class="html-italic">SENP1</span> transfection. Cell viability of EFO27 (<b>A</b>,<b>B</b>), OCM.72 (<b>E</b>,<b>F</b>), and TOV2414 (<b>I</b>,<b>J</b>) cells transfected with esiRNA for <span class="html-italic">KIN17</span> and <span class="html-italic">SENP1</span>. Data are the mean ± SD of at least three independent experiments run in triplicate, and are expressed as the percentage on scramble-transfected cells. After 72 h following transfection, RNA was extracted from cells transfected with target and scramble esiRNAs and analyzed to evaluate the expression of targeted genes. The relative-fold change in the selected genes after transfection with the selected esiRNA compared to scramble esiRNA-transfected cells. Mean ± SD of at least three independent experiments (<b>C</b>,<b>D</b>,<b>G</b>,<b>H</b>,<b>K</b>,<b>L</b>). ns: not significant; *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ****: <span class="html-italic">p</span> &lt; 0.0001</p>
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<p>Effects of esiRNA <span class="html-italic">JUND</span>, <span class="html-italic">CARD9</span> and <span class="html-italic">BCL2L2</span> transfection in EFO27. Cellular viability of EFO27 cells transfected with the esiRNAs <span class="html-italic">JUND</span> (<b>A</b>), CARD9 (<b>B</b>), and BCL2L2 (<b>C</b>) with or without onvansertib treatment. Data are the mean ± SD of at least two independent experiments and are expressed as percentage of negative control cells. After 72 h following transfection, RNA was extracted from cells transfected with target and scramble esiRNAs and analyzed to evaluate the expression of targeted genes (<b>D</b>–<b>F</b>). Data expressed as relative-fold change in target compared to scramble. Results are the mean ± SD of at least two independent experiments. ONV: onvansertib. ns: not significant; *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; ****: <span class="html-italic">p</span> &lt; 0.0001</p>
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<p>Combination of navitoclax and onvansertib on mEOC cells. Dose–response curve for navitocax in EFO27 (<b>A</b>), OCM.72 (<b>B</b>), TOV2414 (<b>C</b>), and MCAS (<b>D</b>) cells. Combination of onvansertib and navitoclax in EFO27 (<b>E</b>), OCM.72 (<b>F</b>), TOV2414 (<b>G</b>), and MCAS (<b>H</b>) cells; the blue curve is onvansertib alone, and the other curves are the combinations with different concentrations of navitoclax. Data are the mean ± SD of at least two independent experiments and are expressed as the percentage of control untreated cells. (<b>I</b>–<b>L</b>) Drug synergy is indicated by blue squares in the Bliss Synergy Heatmap ONV: onvansertib.</p>
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<p>Molecular investigation of the combination in EFO27. Cells were treated with onvansertib 75 nM, navitoclax 1.25 μM, or their combination. At the times indicated, cells were analyzed for Annexin V positivity (<b>A</b>), caspase 3/7 activity (<b>B</b>), and cell cycle perturbation (<b>C</b>). Representative histograms show the percentages of Annexin V-positive cells, analyzed by flow cytometry (<b>A</b>), and luminescence signals corresponding to caspase 3/7 activation (<b>B</b>). Bar graphs display the quantification of cells in different cell cycle phases (<b>C</b>). Data are presented as the mean ± SD of three independent experiments.Ctrl: control; Onv: Onvansertib; *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01.</p>
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33 pages, 2530 KiB  
Article
Enhancing the Therapeutic Effect and Bioavailability of Irradiated Silver Nanoparticle-Capped Chitosan-Coated Rosuvastatin Calcium Nanovesicles for the Treatment of Liver Cancer
by Tamer Mohamed Mahmoud, Mohamed Mahmoud Abdelfatah, Mahmoud Mohamed Omar, Omiya Ali Hasan, Saad M. Wali, Mohamed S. Elmofti, Mohamed G. Ewees, Amel E. Salem, Tarek I. Abd-El-Galil and Dina Mohamed Mahmoud
Pharmaceutics 2025, 17(1), 72; https://doi.org/10.3390/pharmaceutics17010072 - 7 Jan 2025
Viewed by 179
Abstract
Background/Objectives: Liver cancer is a prevalent form of carcinoma worldwide. A novel chitosan-coated optimized formulation capped with irradiated silver nanoparticles (INops) was fabricated to boost the anti-malignant impact of rosuvastatin calcium (RC). Methods: Using a 23-factorial design, eight formulations were produced [...] Read more.
Background/Objectives: Liver cancer is a prevalent form of carcinoma worldwide. A novel chitosan-coated optimized formulation capped with irradiated silver nanoparticles (INops) was fabricated to boost the anti-malignant impact of rosuvastatin calcium (RC). Methods: Using a 23-factorial design, eight formulations were produced using the solvent evaporation process. The formulations were characterized in vitro to identify the optimal formulation (Nop). Results: The FTIR spectra showed that the fingerprint region is not superimposed with that of the drug; DSC thermal analysis depicted a negligible peak shift; and XRPD diffractograms revealed the disappearance of the typical drug peaks. Nop had an entrapment efficiency percent (EE%) of 86.2%, a polydispersity index (PDI) of 0.254, a zeta potential (ZP) of −35.3 mV, and a drug release after 12 h (Q12) of 55.6%. The chitosan-coated optimized formulation (CS.Nop) showed significant mucoadhesive strength that was 1.7-fold greater than Nop. Physical stability analysis of CS.Nop revealed negligible alterations in VS, ZP, PDI, and drug retention (DR) at 4 °C. The irradiated chitosan-coated optimized formulation capped with silver nanoparticles (INops) revealed the highest inhibition effect on carcinoma cells (97.12%) compared to the chitosan-coated optimized formulation (CS.Nop; 81.64) and chitosan-coated optimized formulation capped with silver nanoparticles (CS.Nop.AgNPs; 92.41). The bioavailability of CS-Nop was 4.95-fold greater than RC, with a residence time of about twice the free drug. CS.Nop has displayed a strong in vitro–in vivo correlation with R2 0.9887. Conclusions: The authors could propose that novel INop could serve as an advanced platform to improve oral bioavailability and enhance hepatic carcinoma recovery. Full article
(This article belongs to the Section Physical Pharmacy and Formulation)
13 pages, 781 KiB  
Review
Comprehensive Overview of Molecular, Imaging, and Therapeutic Challenges in Rectal Mucinous Adenocarcinoma
by Mihaela Berar, Andra Ciocan, Emil Moiș, Luminița Furcea, Călin Popa, Răzvan Alexandru Ciocan, Florin Zaharie, Cosmin Puia, Nadim Al Hajjar, Cosmin Caraiani, Ioana Rusu and Florin Graur
Int. J. Mol. Sci. 2025, 26(2), 432; https://doi.org/10.3390/ijms26020432 - 7 Jan 2025
Viewed by 282
Abstract
Rectal cancer is one of the most frequent malignancies worldwide. The most common histological type is adenocarcinoma, followed by mucinous adenocarcinoma. The outcome is less favorable for the mucinous type, yet the treatment course is the same. The aim of this systematic literature [...] Read more.
Rectal cancer is one of the most frequent malignancies worldwide. The most common histological type is adenocarcinoma, followed by mucinous adenocarcinoma. The outcome is less favorable for the mucinous type, yet the treatment course is the same. The aim of this systematic literature review is to assess existing information in order to improve survival in rectal mucinous adenocarcinoma (RMA) and establish a starting point for future research. A systematic search of PubMed, Google Scholar, and Web of Science online libraries was performed in October 2024, evaluating studies regarding clinicopathological and genetic features in connection with targeted treatment and survival outcomes in RMA, using the terms “rectal cancer”, “rectum”, “mucinous adenocarcinoma”, or a combination of the terms. We selected 23 studies, 10 of them regarding the diagnostic implications and 13 discussing the treatment strategies and prognosis of this histological subtype. There were six studies addressing the imaging aspects, highlighting the distinct features of mucinous histology in MRI. The molecular specifics were detailed in four studies, outlining the molecular footprint. The prognosis and treatment course were addressed in 12 studies. The inflammation index prognosis, complete response to neoadjuvant chemotherapy, and surgical aspects were addressed individually in each study. We encapsulated the molecular and clinicopathological characteristics of RMA, as well as diagnostic and treatment approaches, to establish a baseline of references for the benefit of daily practice and further research. Full article
(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Colorectal Cancer 3.0)
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<p>PRISMA flowchart of the included studies.</p>
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<p>Subject flowchart of the studies included.</p>
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22 pages, 6446 KiB  
Article
Limosilactobacillus reuteri ZY15 Alleviates Intestinal Inflammation and Barrier Dysfunction via AKT/mTOR/HIF-1α/RORγt/IL-17 Signaling and the Gut Microbiota in ETEC K88-Challenged Mice
by Xin Xu, Hongwei Zhang, Kun Meng, Hongying Cai, Weiwei Liu, Liye Song, Zihan Zhang, Qijun Zhu, Xiling Han, Yunsheng Han and Peilong Yang
Antioxidants 2025, 14(1), 58; https://doi.org/10.3390/antiox14010058 - 6 Jan 2025
Viewed by 266
Abstract
Limosilactobacillus reuteri, a recognized probiotic, improves intestinal health in animals, but the mechanism remains unclear. This study investigates the mechanisms by which L. reuteri ZY15, isolated from healthy pig feces, mitigates intestinal barrier damage and inflammation caused by oxidative stress in Enterotoxigenic [...] Read more.
Limosilactobacillus reuteri, a recognized probiotic, improves intestinal health in animals, but the mechanism remains unclear. This study investigates the mechanisms by which L. reuteri ZY15, isolated from healthy pig feces, mitigates intestinal barrier damage and inflammation caused by oxidative stress in Enterotoxigenic Escherichia coli (ETEC) K88-challenged mice. The results indicated that L. reuteri ZY15 increased antioxidant capacity by reducing serum reactive oxygen species (ROS) and superoxide dismutase (SOD) levels. L. reuteri ZY15 enhanced the intestinal barrier by upregulating mucin 1, mucin 2, occludin, zonula occludens-1 (ZO-1), and claudin-1 expressions in protein and mRNA levels. It significantly alleviated intestinal inflammation by reducing the proinflammatory cytokines interleukin-1β (IL-1β), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and interleukin-17 (IL-17) mRNA and protein levels. Notably, L. reuteri ZY15 suppressed intestinal inflammation by inhibiting AKT/mTOR/HIF-1α/RORγt/IL-17 pathway activation. Additionally, it significantly altered the structure of gut microorganisms by enriching Akkermansia and Clostridia_UCG.014, and thereby re-establishing colonization resistance and alleviating ETEC K88-induced intestinal barrier damage and inflammation in mice. Taken together, our findings reveal the protective mechanism of L. reuteri ZY15 in mice challenged with ETEC K88 by regulating AKT/mTOR/HIF-1α/RORγt/IL-17 signaling and microbial imbalance. Leveraging these properties, live L. reuteri ZY15 offers a promising alternative treatment for Escherichia coli-induced diarrhea in weaned piglets. Full article
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on body weight, serum indices, and bacterial composition in feces after ETEC K88 challenge. (<b>A</b>) The experimental design and groupings; (<b>B</b>) Body weights; (<b>C</b>) Food intake; (<b>D</b>) Serum lipopolysaccharide (LPS) levels; (<b>E</b>) Serum diamine oxidase (DAO) concentrations; (<b>F</b>) Serum reactive oxygen species (ROS) concentrations; (<b>G</b>) Serum superoxide dismutase (SOD) concentrations. (<b>H</b>,<b>I</b>) Fecal coliform and lactic acid bacterial numbers on day 21. Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. control group; <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. K88 group. (<b>D</b>–<b>I</b>) Differences among treatments were analyzed by two-way ANOVA. <sup>a–d</sup> represent different significant differences (K88 × <span class="html-italic">L. reuteri</span> ZY15: <span class="html-italic">p</span> &lt; 0.05). K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on intestinal tissue pathology and morphology after ETEC challenge. (<b>A</b>) Representative jejunum, ileum, and colon histological sections (bar 200 microns). Deep neutrophil and monocyte infiltration into serosal layers (blue arrows), local mucosal erosion and crypt disappearance (black arrows), mucosal lamina propria angiogenesis (red arrows), and fibrous tissue propria at significant inflammation sites (green arrows); (<b>B</b>) ileal crypt depth; (<b>C</b>) ileal villus height; (<b>D</b>) villus-height-to-ileum-crypt-depth ratios; (<b>E</b>) pathological scores in the ileum. (<b>B</b>–<b>E</b>) Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). Differences among treatments were analyzed by two-way ANOVA. <sup>a–c</sup> represent different significant differences (K88 ×<span class="html-italic">L. reuteri</span> ZY15: <span class="html-italic">p</span> &lt; 0.05). K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p>Differentially expressed gene (DEG) analyses in the ileum after ETEC challenge. (<b>A</b>) Volcano plots showing DEGs in Con and K88 groups. A total of 2921 upregulated and 2683 downregulated DEGs were identified between the K88 and Con groups. (<b>B</b>) Volcano plots showing DEGs in K88 and ZY15–K88 groups. A total of 492 upregulated and 214 downregulated DEGs were identified between the ZY15–K88 and the K88 groups. (<b>C</b>) Specific mRNA gene expression in Con and K88 and K88 and ZY15–K88 samples. <span class="html-italic">t</span>-tests were used in analyses. Differences are significant at * <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 (<span class="html-italic">n</span> = 4). Con, basal diet group; ZY15, basal diet group (<span class="html-italic">L. reuteri</span> ZY15 diet group); K88, basal diet group treated with ETEC K88; ZY15–K88, <span class="html-italic">L. reuteri</span> ZY15 diet group treated with ETEC K88.</p>
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<p>Gene Ontology (GO) pathway analyses of differentially expressed genes (DEGs) in the ileum after ETEC challenge. (<b>A</b>) GO pathway analyses of DEGs in Con and K88 and K88 and ZY15–K88 samples. (<b>B</b>) GO–BP analysis of significantl DEGs in Con and K88 and K88 and ZY15–K88 samples. Groups had at least four biological replicates. Con, basal diet group; ZY15, basal diet group (<span class="html-italic">L. reuteri</span> ZY15 diet group); K88, basal diet group treated with ETEC K88; and ZY15-K88, <span class="html-italic">L. reuteri</span> ZY15 diet group treated with ETEC K88.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on ileal inflammation and barrier function after ETEC K88 challenge. (<b>A</b>,<b>B</b>) Ileal <span class="html-italic">IL-1β</span> and <span class="html-italic">IFN-γ</span> mRNA expression levels. (<b>C</b>) Ileal TNF-α expression levels. (<b>D</b>–<b>F</b>) <span class="html-italic">Occludin</span>, <span class="html-italic">ZO-1</span>, and <span class="html-italic">Claudin-1</span> mRNA expression levels in ileum tissue. (<b>G</b>,<b>H</b>) MUC1 and MUC2 expression levels in ileum tissue. Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). Differences among treatments were analyzed by two-way ANOVA. <sup>a–c</sup> represent different significant differences (K88 × <span class="html-italic">L. reuteri</span> ZY15: <span class="html-italic">p</span> &lt; 0.05). K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on the AKT/mTOR/HIF-1α/RORγt/IL-17 pathway in ileum tissue after ETEC K88 challenge. (<b>A</b>−<b>F</b>) Ileal AKT, mTORC1, mTORC2, HIF-1α, RORγt, and IL-17 expression levels. Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). Differences among treatments were analyzed by two-way ANOVA. <sup>a,b</sup> represent different significant differences (K88 ×<span class="html-italic">L. reuteri</span> ZY15: <span class="html-italic">p</span> &lt; 0.05). K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on cecal microbiota diversity after ETEC K88 challenge. (<b>A</b>) Venn diagram showing gut microbiome ASVs across groups. (<b>B</b>) Chao α-diversity index. (<b>C</b>) Shannon α-diversity index. (<b>D</b>) PCoA distance at species levels. (<b>B</b>,<b>C</b>) Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). Differences among treatments were analyzed by two-way ANOVA. K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 effects on community abundance at phylum and genus levels in cecal microbiota after ETEC K88 challenge. (<b>A</b>) Top 15 relatively abundant microorganisms at phylum levels. (<b>B</b>) Top 35 relatively abundant microorganisms at genus levels. (<b>C</b>–<b>H</b>) Community abundance of <span class="html-italic">Akkermansia</span> (<b>C</b>), <span class="html-italic">Alloprevotella</span> (<b>D</b>), <span class="html-italic">Eubacterium_coprostanoligenes_group</span> (<b>E</b>), <span class="html-italic">Bacteroides</span> (<b>F</b>), <span class="html-italic">Blautia</span> (<b>G</b>), and <span class="html-italic">Clostridia_UCG.014</span> (H) in cecal microbiota. Each group had at least six biological replicates. (<b>C</b>–<b>H</b>) Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 6). Differences among treatments were analyzed by two-way ANOVA. <sup>a,b</sup> represent different significant differences (K88 ×<span class="html-italic">L. reuteri</span> ZY15: <span class="html-italic">p</span> &lt; 0.05). K88 indicates dietary supplementation with ETEC K88 or not; <span class="html-italic">L. reuteri</span> ZY15 indicates the dietary supplementation of <span class="html-italic">L. reuteri</span> ZY15 or not.</p>
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<p><span class="html-italic">L. reuteri</span> ZY15 correlations between the gut microbiota (top 35 phyla and genus levels) and cytokine levels after ETEC K88 challenge. (<b>A</b>) Spearman’s correlation heatmap showing genus levels. (<b>B</b>) Schematic showing how <span class="html-italic">L. reuteri</span> ZY15 improves inflammation at the intestines after ETEC K88 challenge. The red upward arrow indicates upregulation of gene expression, while the red downward arrow indicates downregulation of gene expression. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 (<span class="html-italic">n</span> = 6). Con, basal diet group; ZY15, basal diet group (<span class="html-italic">L. reuteri</span> ZY15 diet group); K88, basal diet group treated with ETEC K88; and ZY15–K88, <span class="html-italic">L. reuteri</span> ZY15 diet group treated with ETEC K88.</p>
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31 pages, 13954 KiB  
Article
Kombucha Versus Vegetal Cellulose for Affordable Mucoadhesive (nano)Formulations
by Ioana Popa-Tudor, Naomi Tritean, Ștefan-Ovidiu Dima, Bogdan Trică, Marius Ghiurea, Anisoara Cimpean, Florin Oancea and Diana Constantinescu-Aruxandei
Gels 2025, 11(1), 37; https://doi.org/10.3390/gels11010037 - 4 Jan 2025
Viewed by 321
Abstract
Cellulose nanofibers gained increasing interest in the production of medical devices such as mucoadhesive nanohydrogels due to their ability to retain moisture (high hydrophilicity), flexibility, superior porosity and durability, biodegradability, non-toxicity, and biocompatibility. In this work, we aimed to compare the suitability of [...] Read more.
Cellulose nanofibers gained increasing interest in the production of medical devices such as mucoadhesive nanohydrogels due to their ability to retain moisture (high hydrophilicity), flexibility, superior porosity and durability, biodegradability, non-toxicity, and biocompatibility. In this work, we aimed to compare the suitability of selected bacterial and vegetal nanocellulose to form hydrogels for biomedical applications. The vegetal and bacterial cellulose nanofibers were synthesized from brewer’s spent grains (BSG) and kombucha membranes, respectively. Two hydrogels were prepared, one based on the vegetal and the other based on the bacterial cellulose nanofibers (VNC and BNC, respectively). VNC was less opaque and more fluid than BNC. The cytocompatibility and in vitro antioxidant activity of the nanocellulose-based hydrogels were investigated using human gingival fibroblasts (HGF-1, ATCC CRL-2014). The investigation of the hydrogel–mucin interaction revealed that the BNC hydrogel had an approx. 2× higher mucin binding efficiency than the VNC hydrogel at a hydrogel/mucin ratio (mg/mg) = 4. The BNC hydrogel exhibited the highest potential to increase the number of metabolically active viable cells (107.60 ± 0.98% of cytotoxicity negative control) among all culture conditions. VNC reduced the amount of reactive oxygen species (ROS) by about 23% (105.5 ± 2.2% of C−) in comparison with the positive control, whereas the ROS level was slightly higher (120.2 ± 3.9% of C−) following the BNC hydrogel treatment. Neither of the two hydrogels showed antibacterial activity when assessed by the diffusion method. The data suggest that the BNC hydrogel based on nanocellulose from kombucha fermentation could be a better candidate for cytocompatible and mucoadhesive nanoformulations than the VNC hydrogel based on nanocellulose from brewer’s spent grains. The antioxidant and antibacterial activity of BNC and both BNC and VNC, respectively, should be improved. Full article
(This article belongs to the Special Issue Advances in Cellulose-Based Hydrogels (3rd Edition))
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<p>Schematic representation of cellulose purification and hydrogel preparation: (<b>a</b>) brewer’s spent grains (BSG); (<b>b</b>) purified vegetal cellulose from BSG; (<b>c</b>) bacterial cellulose (BC) membrane from kombucha fermentation; (<b>d</b>) purified BC; (<b>e</b>) hydrogel of vegetal nanocellulose from BSG (VNC); (<b>f</b>) hydrogel of bacterial nanocellulose from kombucha fermentation (BNC); (<b>g</b>) freeze-dried VNC; and (<b>h</b>) freeze-dried BNC.</p>
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<p>TEM analysis of: (<b>a</b>) VNC (2 µm scale); (<b>b</b>) VNC (500 nm scale), (<b>c</b>) VNC (100 nm scale); (<b>d</b>) BNC (2 µm scale); (<b>e</b>) BNC (500 nm scale); and (<b>f</b>) BNC (100 nm scale); VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>SEM analysis using secondary electrons (SE) detector (1000×) of: (<b>a</b>) VNC, (<b>b</b>) BNC, (<b>c</b>) VNCMu, (<b>d</b>) BNCMu, and (<b>e</b>) Mu; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains-based hydrogel; BNC—hydrogel of bacterial nanocellulose from kombucha fermentation-based hydrogel; VNCMu—VNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); BNCMu—BNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); and Mu—mucin suspension.</p>
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<p>SEM analysis using backscattered electrons (BSE) detector (1000×) of: (<b>a</b>) VNC, (<b>b</b>) BNC, (<b>c</b>) VNCMu, (<b>d</b>) BNCMu, (<b>e</b>) Mu; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; BNC—hydrogel of bacterial nanocellulose from kombucha fermentation; VNCMu—VNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); BNCMu—BNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); and Mu—mucin suspension.</p>
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<p>X-ray diffraction (XRD) analysis and crystallinity index (Xc,%) of: (<b>a</b>) VNC, Mu, VNCMu; (<b>b</b>) BNC, Mu, BNCMu; the vertical bars represent the main diffraction peaks of cellulose Iα, Iβ, and amorphous cellulose in the PDXL database; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; BNC—hydrogel of bacterial nanocellulose from kombucha fermentation; VNCMu—VNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); BNCMu—BNC mixed with a 3.5% mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); and Mu—mucin suspension.</p>
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<p>Overlapping ATR-FTIR spectra for (<b>a</b>) VNC, Mu, VNCMu, and (<b>b</b>) BNC, Mu, and BNCMu. VNCMu—hydrogel of vegetal nanocellulose from brewer’s spent grains (VNC) mixed with a mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); and BNCMu—hydrogel of bacterial nanocellulose from kombucha fermentation (BNC) mixed with a mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>).</p>
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<p>Mucin binding efficiency (±standard deviation, <span class="html-italic">n</span> = 3, α &lt; 0.05; different letters indicate statistically significant differences between samples); VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>Rheological behavior of VNC and BNC hydrogels: (<b>a</b>) frequency sweep of VNC; (<b>b</b>) frequency sweep of BNC; (<b>c</b>) flow sweep of VNC; (<b>d</b>) flow sweep of BNC; (<b>e</b>) axial mode of VNC; (<b>f</b>) axial mode of BNC; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>Rheological behavior of VNCMu and BNCMu: (<b>a</b>) frequency sweep of VNCMu; (<b>b</b>) frequency sweep of BNCMu; (<b>c</b>) flow sweep of VNCMu; (<b>d</b>) flow sweep of BNCMu; (<b>e</b>) axial mode of VNCMu; (<b>f</b>) axial mode of BNCMu; VNCMu—hydrogel of vegetal nanocellulose from brewer’s spent grains (VNC) mixed with a mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>); and BNCMu—hydrogel of bacterial nanocellulose from kombucha fermentation (BNC) mixed with a mucin suspension in a ratio of 1:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>).</p>
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<p>Porosity analysis of freeze-dried BNC: (<b>a</b>) BET isotherm and pore volume distribution for micropores (D &lt; 2 nm), small mesopores (2 &lt; D &lt; 10 nm), large mesopores (10 &lt; D &lt; 40 nm), and macropores (D &gt; 40 nm); and (<b>b</b>) DFT method for cumulative pore volume and pore size distribution. BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>Cytocompatibility of VNC and BNC hydrogels: (<b>a</b>) Cell Counting Kit-8 (CCK-8) assay (±error bars, <span class="html-italic">n</span> = 3, α &lt; 0.05; *—σ between 0.05 and 0.01, **—σ between 0.01 and 0.001, and ***—σ &lt; 0.001; black stars indicate statistically significant values that exceed C−; red stars indicate statistically significant values that are below C−); C− (untreated cells, negative cytotoxicity control), C+ (cells treated with 7.5% dimethyl sulfoxide (DMSO), positive cytotoxicity control), VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; BNC—hydrogel of bacterial nanocellulose from kombucha fermentation; (<b>b</b>–<b>h</b>) LIVE/DEAD assay (live cells—green fluorescence, dead cells—red fluorescence): (<b>b</b>) C−; (<b>c</b>) C+; (<b>d</b>) cells treated with 0.0125% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>e</b>) cells treated with 0.025% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>f</b>) cells treated with 0.05% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>g</b>) cells treated with 0.1% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>h</b>) cells treated with 0.2% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>i</b>) cells treated with 0.0125% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) BNC; (<b>j</b>) cells treated with 0.025% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) BNC; (<b>k</b>) cells treated with 0.05% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) BNC; (<b>l</b>) cells treated with 0.1% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) BNC; and (<b>m</b>) cells treated with 0.2% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) BNC.</p>
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<p>Cell morphology following the treatment with VNC and BNC hydrogels (Alexa Fluor 488-coupled phalloidin labelling of the actin filaments—green fluorescence, and DAPI labelling of the nuclei—blue fluorescence): (<b>a</b>) untreated cells, negative control); (<b>b</b>) cells treated with 0.025% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC; (<b>c</b>) cells treated with 0.025% BNC; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>In vitro antioxidant activity following the treatment with VNC and BNC hydrogels: (<b>a</b>) total intracellular reactive oxygen species (ROS) production (±standard deviation, <span class="html-italic">n</span> = 3, α &lt; 0.05; different letters indicate statistically significant differences between samples); (<b>b–e</b>) fluorescence microscopy images after labeling total intracellular ROS with H<sub>2</sub>DCFDA (green fluorescence); HGF-1 cells treated with: (<b>b</b>) untreated cells (negative control); (<b>c</b>) cells treated with 37 µM H<sub>2</sub>O<sub>2</sub> (positive control, ROS inducer) VNC; (<b>d</b>) cells treated with 0.025% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) VNC in the presence of the ROS inducer; (<b>e</b>) cells treated with 0.025% BNC in the presence of the ROS inducer; VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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<p>Antibacterial activity following the treatment with different VNC and BNC hydrogel doses: (<b>a</b>–<b>e</b>) antibacterial activity of 10 µL hydrogel dose against: (<b>a</b>) <span class="html-italic">B. cereus</span>; (<b>b</b>) <span class="html-italic">E. faecalis</span>; (<b>c</b>) <span class="html-italic">S. aureus</span>; (<b>d</b>) <span class="html-italic">E. coli</span>; (<b>e</b>) <span class="html-italic">S. marcescens</span>; (<b>f</b>–<b>j</b>) antibacterial activity of 50 µL hydrogel dose against: (<b>f</b>) <span class="html-italic">B. cereus</span>; (<b>g</b>) <span class="html-italic">E. faecalis</span>; (<b>h</b>) <span class="html-italic">S. aureus</span>; (<b>i</b>) <span class="html-italic">E. coli</span>; and (<b>j</b>) <span class="html-italic">S. marcescens</span>. VNC—hydrogel of vegetal nanocellulose from brewer’s spent grains; and BNC—hydrogel of bacterial nanocellulose from kombucha fermentation.</p>
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15 pages, 4281 KiB  
Article
Yeokwisan: Standardised Herbal Formula Enhancing Gastric Mucosal Protection Against Gastric Ulcers in Mice, a Preclinical Study
by Yun Mi Lee, Kyuhyung Jo, So Yeon Kim, Chang-Seob Seo, Eunjung Son, Aejin Kim and Dong-Seon Kim
Pharmaceuticals 2025, 18(1), 44; https://doi.org/10.3390/ph18010044 - 2 Jan 2025
Viewed by 367
Abstract
Background: Yeokwisan (YWS) is a standardised herbal formula for relieving functional dyspepsia symptoms. Methods: We explored the therapeutic value of YWS and its potential effects on gastritis. Its inhibitory effect on gastric mucosal damage and anti-inflammatory activity in animal models of [...] Read more.
Background: Yeokwisan (YWS) is a standardised herbal formula for relieving functional dyspepsia symptoms. Methods: We explored the therapeutic value of YWS and its potential effects on gastritis. Its inhibitory effect on gastric mucosal damage and anti-inflammatory activity in animal models of alcohol- and restraint stress-induced gastritis were also examined. Gastric tissues of ICR mice treated with YWS (150 and 300 mg/kg) or famotidine (5 mg/kg) for 10 days were collected, and gastric lesions were quantified. The stomachs of C57BL/6 mice treated with YWS (150 and 300 mg/kg) or famotidine (5 mg/kg) for 23 days were collected, and gastric lesions were quantified. Blood samples were analysed for inflammation related factors and gastroprotective effects. Results: YWS (300 mg/kg) inhibited gastric damage by 42.33% in the EtOH-induced gastritis model and 75% in the restraint stress-induced gastritis model (compared to the control group). Pretreatment with YWS led to decreased levels of inflammatory factors (IL-1β, IL-6, and COX-2). YWS showed gastroprotective effects through histamine downregulation, while prostaglandin E2 (PGE2) and mucin were upregulated. The mRNA levels of H2R, M3R, CCK2R, and H+/K+ ATPase were significantly decreased following treatment with YWS. Conclusions: YWS provides gastric protection through its anti-inflammatory properties, reduced histamine secretion, and enhanced release of mucosal defensive factors. Full article
(This article belongs to the Section Natural Products)
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<p>Three-dimensional HPLC chromatograms of freeze-dried Yeokwisan (YWS) sample.</p>
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<p>Gastroprotective effect of YWS against ethanol-induced ulceration in mice. (<b>A</b>) Representative images of murine stomachs. (<b>B</b>) Pathological injury index. Histological analysis of (<b>C</b>) haematoxylin and eosin (H&amp;E) and (<b>D</b>) periodic acid-Schiff (PAS) staining in gastric tissue. Scale bar, 200 µm. Data are expressed as means ± standard error of the mean (SEM) (n = 5). <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 versus the normal group (Nor); and * <span class="html-italic">p</span> &lt; 0.05 versus the control group (Con).</p>
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<p>Effect of YWS on gastric injury in mice with restraint stress-induced gastric ulcers (<b>A</b>). Data are expressed as means ± standard error of the mean (SEM) (n = 8). Histological analysis of (<b>B</b>) haematoxylin and eosin (H&amp;E) and (<b>C</b>) periodic acid–Schiff (PAS) staining in gastric tissue. # <span class="html-italic">p</span> &lt; 0.05 versus the non-stressed group (Nor); and * <span class="html-italic">p</span> &lt; 0.05 versus the stressed group (Con). Scale bar, 200 µm.</p>
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<p>Effect of YWS on stress-induced gastrin, corticosterone, histamine, and PGE2 levels. YWS affected the accumulation of (<b>A</b>) gastrin, (<b>B</b>) corticosterone, (<b>C</b>) histamine, and (<b>D</b>) PGE2 levels. Data are expressed as the mean ± SEM (n = 8). <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001 versus the non-stressed group (Nor); and * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 versus the stressed group (Con).</p>
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<p>Effect of YWS on the expression of genes associated with gastric mucin and factors promoting gastric acid secretion in a model of restraint stress-induced gastritis. mRNA levels of (<b>A</b>) MUC5AC, (<b>B</b>) histamine H2-receptors (H2R), (<b>C</b>) cholecystokinin-2/gastrin receptors (CCK2R), (<b>D</b>) muscarinic acetylcholine receptor M3 receptor (M3R), and (<b>E</b>) H<sup>+</sup>/K<sup>+</sup> ATPase in the gastric mucosa. Data are expressed as the mean ± SEM (n = 8). <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 versus the non-stressed group (Nor); * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01, versus the stressed group (Con).</p>
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<p>Effect of YWS on stress-induced cAMP levels. YWS reduced the accumulation of cAMP. Data are expressed as the mean ± SEM (n = 8). <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus the non-stressed group (Nor) and * <span class="html-italic">p</span> &lt; 0.05 versus the stressed group (Con).</p>
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<p>Effects of YWS on the expression of cytokines and inflammatory mediators in a model of EtOH-induced gastric ulcer. The expression of (<b>A</b>) IL-1β, (<b>B</b>) IL-6, and (<b>C</b>) COX-2 was determined using ELISA and WB (n = 8) and Western blot analysis (n = 3). Values are expressed as mean ± SEM. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 versus the normal control group (Nor); and * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01, versus the gastritis control group (Con).</p>
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<p>Effects of YWS on the expression of histamine and PGE2 in a model of EtOH-induced gastric ulcer. The expression of (<b>A</b>) histamine and (<b>B</b>) PGE2 levels were determined using ELISA. Values are expressed as mean ± SEM (n = 8). <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 versus the normal control group; and * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01, versus the gastritis control group.</p>
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11 pages, 3447 KiB  
Communication
CCL4 Affects Eosinophil Survival via the Shedding of the MUC1 N-Terminal Domain in Airway Inflammation
by Yoshiki Kobayashi, Chu Hong Hanh, Naoto Yagi, Nhi Kieu Thi Le, Yasutaka Yun, Akihiro Shimamura, Kenta Fukui, Akitoshi Mitani, Kensuke Suzuki, Akira Kanda and Hiroshi Iwai
Cells 2025, 14(1), 33; https://doi.org/10.3390/cells14010033 - 31 Dec 2024
Viewed by 313
Abstract
Eosinophilic chronic rhinosinusitis (ECRS), a CRS with nasal polyps (CRSwNP), is characterized by eosinophilic infiltration with type 2 inflammation and is highly associated with bronchial asthma. Intractable ECRS with poorly controlled asthma is recognized as a difficult-to-treat eosinophilic airway inflammation. Although eosinophils are [...] Read more.
Eosinophilic chronic rhinosinusitis (ECRS), a CRS with nasal polyps (CRSwNP), is characterized by eosinophilic infiltration with type 2 inflammation and is highly associated with bronchial asthma. Intractable ECRS with poorly controlled asthma is recognized as a difficult-to-treat eosinophilic airway inflammation. Although eosinophils are activated and coincubation with airway epithelial cells prolongs their survival, the interaction mechanism between eosinophils and epithelial cells is unclear. This study examined the effect of eosinophils on mucin glycoprotein 1 (MUC1), a member of membrane-bound mucin, in the airway epithelial cells, to elucidate the mechanisms of the eosinophil–airway epithelial cell interaction. Nasal polyp samples from patients with CRSwNP and BEAS-2B airway epithelial cells, coincubated with purified eosinophils, were stained with two MUC1 antibodies. To confirm the involvement of CCL4, an anti-CCL4 neutralizing antibody or recombinant CCL4 was used as needed. The immunofluorescence results revealed a negative correlation between the expression of full-length MUC1 and eosinophil count in nasal polyps. In BEAS-2B coincubated with eosinophils, full-length MUC1, but not the C-terminal domain, was reduced, and eosinophil survival was prolonged, which was concomitant with CCL4 increase, whereas the anti-CCL4 neutralizing antibody decreased these reactions. The survival of eosinophils that contacted recombinant MUC1 without the N-terminal domain was prolonged, and recombinant CCL4 increased the expression of metalloproteases. Increased CCL4 induces the contact between eosinophils and airway epithelial cells by shedding the MUC1 N-terminal domain and enhances eosinophil survival in eosinophilic airway inflammation. This novel mechanism may be a therapeutic target for difficult-to-treat eosinophilic airway inflammation. Full article
(This article belongs to the Special Issue Eosinophils and Their Role in Allergy and Related Diseases)
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<p>Full-length MUC1 (MUC1-FL) expression in the epithelial cells of nasal polyps. (<b>A</b>) Immunofluorescence analysis of nasal polyps obtained from patients with chronic rhinosinusitis with nasal polyps (CRSwNP) with high eosinophil count (left panels; i–iii) or those with low eosinophil count (right panels; iv–vi). MUC1-FL (green), C-terminal domain (MUC1-C, red), and the nucleus (blue) are stained. Images were captured by an FV3000 confocal microscope (400× objectives). The scale bars in the bottom-right corner indicate 10 μm. (<b>B</b>) Correlation of MUC1-FL expression with eosinophil count in nasal polyps. MUC1-FL intensity is indicated as a ratio to epithelial cell adhesion molecule (EpCAM) (n = 39).</p>
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<p>Effect of eosinophils on full-length MUC1 (MUC1-FL) expression in airway epithelial cells. (<b>A</b>–<b>D</b>) BEAS-2B cells were coincubated overnight with purified peripheral blood eosinophils. MUC1-FL mRNA levels (<b>A</b>), MUC1-FL protein levels (<b>B</b>), and MUC1 C-terminal domain (MUC1-C) protein levels (<b>C</b>) were evaluated. (<b>D</b>) Immunofluorescence analysis of MUC1-FL (green), MUC1-C (red), and the nucleus (blue) are shown in the upper (without eosinophils) and lower (with eosinophils) panels. Images were captured by an FV3000 confocal microscope (400× objectives). Scale bars in the bottom-right corner indicate 10 μm. Results were representative of at least three experiments. (<b>E</b>) MUC1-FL protein expression in BEAS-2B coincubated with the supernatants of eosinophilic mucin overnight. Patients underwent endoscopic sinus surgery under general anesthesia. Mucin samples were collected from the sinuses of refractory ECRS subjects. Values in (<b>A</b>–<b>C</b>,<b>E</b>) represent the mean ± SEM of four experiments; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (vs. vehicle).</p>
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<p>Relation between full-length MUC1 (MUC1-FL) and CCL4 expression in the epithelial cells of nasal polyps. (<b>A</b>) Immunofluorescence staining of nasal polyps obtained from patients with CRSwNP with high or low eosinophil count. MUC1-FL, CCL4, and epithelial cell adhesion molecule (EpCAM) expression levels were evaluated. MUC1-FL (pink), CCL4 (green), EpCAM (orange), MBP (red), and the nucleus (blue) are stained with hematoxylin and eosin (H&amp;E). Images were captured by an FV3000 confocal microscope (100× objectives). The scale bars in the bottom-right corner indicate 100 μm. (<b>B</b>,<b>C</b>) Correlation of CCL4 expression with the eosinophil count in nasal polyps (<b>B</b>) and MUC1-FL expression (<b>C</b>). MUC1-FL and CCL4 intensities are indicated as a ratio to EpCAM (n = 39).</p>
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<p>Effect of CCL4-mediated reduction of full-length MUC1 (MUC1-FL) on eosinophil survival. (<b>A</b>,<b>B</b>) BEAS-2B was stimulated overnight with recombinant human CCL4 (10 μg/mL). MUC1-FL expression (<b>A</b>) and matrix metalloproteases (ADAM17 and MMP14) mRNA levels (<b>B</b>) in BEAS-2B. (<b>C</b>–<b>E</b>) BEAS-2B and purified eosinophils were coincubated with or without anti-CCL4 neutralizing antibody (10 μg/mL). MUC1-FL protein levels in BEAS-2B (<b>C</b>), CCL4 concentration in supernatants of cell culture (<b>D</b>), and eosinophil survival (<b>E</b>) were evaluated. (<b>F</b>) Purified eosinophils were incubated overnight on a recombinant human MUC1-coated plate, followed by the evaluation of their survival. Images (MUC1-FL, green; nucleus, blue) in A were captured by an FV3000 confocal microscope (400× objectives) with scale bars (20 μm) in the bottom-right corner, which were representative of at least three experiments. The values in (<b>B</b>–<b>F</b>) represent the mean ± SEM of four experiments. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 (vs. without rhCCL4 in (<b>B</b>), without eosinophils in (<b>C</b>,<b>D</b>), without BEAS-2B in (<b>E</b>), and without rhMUC1 in (<b>F</b>)). ** <span class="html-italic">p</span> &lt; 0.01 (between the two groups).</p>
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<p>Mechanism of prolonged eosinophil survival associated with MUC1 in airway epithelial cells. Eosinophils–airway epithelial cells contact activates both cells and induces CCL4 release from them. CCL4 might be involved in the shedding of the MUC1 N-terminal domain by increased expression of these metalloproteases (e.g., ADAM17 and MMP14). The viability of eosinophils bound to the MUC1 C-terminal domain could be upregulated.</p>
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21 pages, 1756 KiB  
Article
Association of Mucin-Degrading Gut Microbiota and Dietary Patterns with Colonic Transit Time in Constipation: A Secondary Analysis of a Randomized Clinical Trial
by Xuangao Wu, Hee-Jong Yang, Myeong-Seon Ryu, Su-Jin Jung, Kwangsu Ha, Do-Yeon Jeong and Sunmin Park
Nutrients 2025, 17(1), 138; https://doi.org/10.3390/nu17010138 - 31 Dec 2024
Viewed by 433
Abstract
Background: The relationship between gut microbiota composition, lifestyles, and colonic transit time (CTT) remains poorly understood. This study investigated associations among gut microbiota profiles, diet, lifestyles, and CTT in individuals with subjective constipation. Methods: We conducted a secondary analysis of data from our [...] Read more.
Background: The relationship between gut microbiota composition, lifestyles, and colonic transit time (CTT) remains poorly understood. This study investigated associations among gut microbiota profiles, diet, lifestyles, and CTT in individuals with subjective constipation. Methods: We conducted a secondary analysis of data from our randomized clinical trial, examining gut microbiota composition, CTT, and dietary intake in baseline and final assessments of 94 participants with subjective constipation. Participants were categorized into normal-transit (<36 h) and slow-transit (≥36 h) groups based on CTT at baseline. Gut microbiota composition was measured using 16S rRNA sequencing, and dietary patterns were assessed through semi-quantitative food frequency questionnaires. Enterotype analysis, machine learning approaches, and metabolic modeling were employed to investigate microbiota–diet interactions. The constipated participants primarily belonged to Lachnospiraceae (ET-L). Results: The slow-transit group showed higher alpha diversity than the normal-transit group. Butyricicoccus faecihominis was abundant in the normal-transit group, while Neglectibacter timonensis, Intestinimonas massiliensis, and Intestinibacter bartlettii were abundant in the slow-transit group, which also had a higher abundance of mucin-degrading bacteria. Metabolic modeling predicted increased N-acetyl-D-glucosamine (GlcNAc), a mucin-derived metabolite, in the slow-transit group. Network analysis identified two microbial co-abundance groups (CAG3 and CAG9) significantly associated with transit time and dietary patterns. Six mucin-degrading species showed differential correlations with GlcNAc and a plant-based diet, particularly, including rice, bread, fruits and vegetables, and fermented beans. In conclusion, an increased abundance of mucin-degrading bacteria and their predicted metabolic products were associated with delayed CTT. Conclusion: These findings suggest dietary modulation of these bacterial populations as a potential therapeutic strategy for constipation. Moreover, our results reveal a potential immunometabolic mechanism where mucin-degrading bacteria and their metabolic interactions may influence intestinal transit, mucosal barrier function, and immune response. Full article
(This article belongs to the Special Issue Nutrition, Gut Microbiota and Immunity)
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<p>Study protocol for colonic transit time (CTT) assessment in intervention and control groups.</p>
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<p>Colonic transit time and constipation symptoms. Colonic transit time between normal- and slow-transit (Slow-T) groups at the baseline and final assessments after the intervention. (<b>A</b>) Colonic transit time for different parts of the colon. (<b>B</b>): Constipation-related parameters between the Normal and Slow-T groups. The -1 and -2 in the name of the legends indicated baseline and final sessions. Normal- and slow-transit groups were compared using Analysis of Covariance (ANCOVA) with adjustment for multiple covariates, including age, gender, daily energy intake, smoking duration, alcohol consumption, pre-existing medical conditions, CKJ intake, physical activity level, and dietary fiber intake. Time, defecation time (min/each); Number, number of defecations per week; BSS, Bristol stool scale; PAC-SYM, patient assessment of constipation symptoms; PAC-QOL, patient assessment of constipation quality of life; PAC sub-categories: sub1, constipation intensity; sub2, daily life influence; sub3 and sub4, emotional changes due to constipation; sub5, daily health condition accompanied with constipation; sub6, satisfaction for improvement in constipation. Significant difference between the Normal and Slow-T groups at * <span class="html-italic">p</span> &lt; 0.05, ** at <span class="html-italic">p</span> &lt; 0.01, and *** at <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Characteristics and distribution of the three enterotypes from the subjective participants of the present study and the healthy subjects without gastrointestinal diseases of previous studies. Subjective constipation; Healthy control (Normal); ET-B, Bacteroidaceae enterotype; ET-L, Lachnospiraceae enterotype; ET-P, Prevotella enterotype. (<b>A</b>). Principal component analysis (PCA) plot of the three enterotypes based on fecal microbiota at the genus level. (<b>B</b>). The number of HC and CC subjects in each enterotype. The Chi-square test was used to statistically assess the significant differences in the number of subjects across the enterotypes. Slow-T, slow-transit group. (<b>C</b>). Relative abundance of the top six family-level taxa in each enterotype. Tukey’s post hoc test was employed to determine significant differences between enterotypes. Different letters on the bars indicated significant differences among the enterotypes at <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>). Relative abundance of the top six genus-level taxa in each enterotype. Tukey’s post hoc test was used to identify significant differences between enterotypes. a–c Different letters on the bars indicated significant differences among the enterotypes at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Gut microbiome analysis. (<b>A</b>). α-diversity Shannon’s diversity index in participants. (<b>B</b>). β-diversity in participants. (<b>C</b>). LEfSe analysis showing differential gut bacteria species. (<b>D</b>). SHAP summary plot for species-level bacterial features. The order of features represents their importance, and each point represents the SHAP value for a specific microbial species. The color indicates the effect of the feature value on the corresponding classification (blue represents low and red represents high). Groups were normal-transit (Normal; &lt;36 h colonic transit time) and slow-transit (≥36 h colonic transit time; Slow-T), based on the colonic transit time test set data. SHAP, SHapley Additive exPlanations. * Significant difference between the Normal and Slow-T groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Analysis of the predicted metabolic features of the gut microbiome using the COBRA toolbox and AGORA2 model. (<b>A</b>). SHAP summary plot for predicted microbial metabolic feature data. The order of features represents their importance, and each point represents the SHAP value for a specific microbial species. The color indicates the effect of the feature value on the corresponding classification (blue represents low and red represents high). (<b>B</b>). Bar chart of N-acetyl-D-glucosamine levels between groups. * Significant differences between the groups at <span class="html-italic">p</span> &lt; 0.05. Groups were normal-transit (Normal; &lt;36 h colonic transit time) and slow-transit (≥36 h colonic transit time; HT), based on the colonic transit time test set data. SHAP, SHapley Additive exPlanations.</p>
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<p>Violin plot of the relative abundance of species in co-abundance groups (CAGs). The total relative abundance of each CAG was calculated by summing the relative abundances of species belonging to the same CAG.</p>
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<p>Correlation network analysis between N-acetyl-D-glucosamine, co-abundance groups (CAG) clusters, species strains, and diet. (<b>A</b>). Correlation analysis of mucin-utilizing species in CAG3 and CAG9 with N-acetyl-D-glucosamine and diet. (<b>B</b>). DBCAN analysis of mucin metabolism substrate utilization in species within CAG3 and CAG9. (<b>C</b>). Correlation analysis between N-acetyl-D-glucosamine, CAG clusters, and diet.</p>
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10 pages, 435 KiB  
Article
Local Recurrence of Premalignant and Early Malignant Rectal Polyps Treated by TEM—A Single-Center Experience
by Muhammad Khalifa, Rachel Gingold-Belfer and Nidal Issa
J. Clin. Med. 2025, 14(1), 80; https://doi.org/10.3390/jcm14010080 - 27 Dec 2024
Viewed by 261
Abstract
Background: Transanal endoscopic microsurgery (TEM) is a minimally invasive approach for excising rectal polyps, particularly those with high-grade dysplasia (HGD) or early-stage rectal cancer (T1). This study aimed to evaluate the recurrence risk and its associated factors in patients treated with TEM for [...] Read more.
Background: Transanal endoscopic microsurgery (TEM) is a minimally invasive approach for excising rectal polyps, particularly those with high-grade dysplasia (HGD) or early-stage rectal cancer (T1). This study aimed to evaluate the recurrence risk and its associated factors in patients treated with TEM for HGD and T1 rectal tumors. Methods: A retrospective review was conducted on 79 patients who underwent TEM for rectal lesions at Rabin Medical Center-Hasharon Hospital from 2005 to 2019. Data collected included demographics, tumor characteristics, and follow-up outcomes, with specific focus on tumor size, resection margins, mucin production, and distance from anal verge (AV). Separate and unified analyses were performed to assess the recurrence risk factors for both HGD and T1 patients. Results: Sixty-three patients were included in the final analysis. In the unified analysis, larger tumor size was significantly associated with increased recurrence risk (OR = 2.27, p = 0.028), and mucin production was a strong predictor of recurrence in the T1 group and combined analysis (p = 0.0012 and p = 0.014, respectively). Distance from AV demonstrated a borderline association with recurrence (p = 0.053). Conclusions: Larger tumor size and mucin production are significant predictors of recurrence in TEM-treated rectal polyps. Personalized follow-up and postoperative management are essential for patients with these risk factors to reduce the recurrence risk. Full article
(This article belongs to the Special Issue Colon and Rectal Surgery: Current Clinical Practice and Future Trends)
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<p>The flowchart of patient’s selection.</p>
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17 pages, 3414 KiB  
Article
The Molecular Weight of Enzymatically Modified Pectic Oligosaccharides from Apple Pomace as a Determinant for Biological and Prebiotic Activity
by Agnieszka Wilkowska, Adriana Nowak, Ilona Motyl and Joanna Oracz
Molecules 2025, 30(1), 46; https://doi.org/10.3390/molecules30010046 - 26 Dec 2024
Viewed by 367
Abstract
The purpose of this research was to investigate the prebiotic effects of different fractions of pectin-derived oligosaccharides (POSs) from apple pomace (AP) in relation to their molecular weight (MW), structure, and composition. Enzymatic treatment of the apple pomace resulted in high-molecular-weight arabinans and [...] Read more.
The purpose of this research was to investigate the prebiotic effects of different fractions of pectin-derived oligosaccharides (POSs) from apple pomace (AP) in relation to their molecular weight (MW), structure, and composition. Enzymatic treatment of the apple pomace resulted in high-molecular-weight arabinans and rhamnogalacturonans (MW 30–100 kDa, MW 10–30 kDa), as well as oligomeric fractions with molecular weights of less than 10 kDa, consisting mainly of homogalacturonan. The biological potential of the POSs against various lactobacilli and bifidobacteria was evaluated. The oligosaccharides with the highest molecular weights (MW 30–100 kDa, MW 10–30 kDa) showed better prebiotic effect to lactobacilli. The oligosaccharides with MW 3–10 kDa and MW 10–30 kDa caused an increase in the bifidogenic effect. Inhibition of Escherichia coli, Salmonella enterica serovar Typhimurium, and Listeria monocytogenes was also observed. The preparations with MW 3–10 kDa and MW 10–30 kDa demonstrated the strongest biological activity, supporting the adhesion of beneficial microorganisms to mucin and collagen surfaces. Therefore, oligosaccharides with MW 10–30 kDa were considered to be the most promising prebiotic candidates. This study confirms that the biological effects of pectic oligosaccharides vary significantly based on their structural differences. Therefore, the conditions of enzymatic hydrolysis of apple pectin should be optimized to obtain oligosaccharides within a specific molecular mass range. Full article
(This article belongs to the Section Food Chemistry)
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<p>HPSEC separation of the F02 AP hydrolysate fraction (Mw &lt; 10 kDa).</p>
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<p>Monosaccharide compositions [%] for POS fractions with different molecular masses. All values are mean ± standard deviation (SD) for triplicate experiments. Values denoted by different letters indicate statistically significant differences between the different MW samples within the same monosaccharide type, ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Anomeric region of <sup>1</sup>H NMR spectrum of POS fractions.</p>
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<p><sup>13</sup>C NMR spectrum of POS fractions.</p>
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<p>Glucosidic region of HSQC (heteronuclear multiple bond correlation) NMR of POS fractions: (<b>A</b>)—MW &lt; 10 kDa; (<b>B</b>)—MW 10–30 kDa; (<b>C</b>)—MW 30–100 kDa; (<b>D</b>)—TOCSY spectrum of MW &lt; 10 kDa fraction.</p>
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<p>Prebiotic activity index (evaluated by comparison with untreated apple pectin). Data represent means from three replicates in one experiment. Error bars denote SD. Values denoted by different letters indicate statistically significant differences between the oligosaccharide MW samples tested within the same microorganism type, ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Influence of molecular mass of POSs used as a carbohydrate source on the number of cultured pathogen bacteria. Data represent means from three replicates in one experiment. Error bars denote SD. Values denoted by different letters indicate statistically significant differences between the oligosaccharide MW samples tested within the same microorganism type, ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Adherence of bacterial strains to collagen in the presence of apple preparations. Data represent means from four replicates (±SD), where a negative percentage value indicates inhibition of adherence compared to the control sample, and a positive percentage value indicates simulation of adherence compared to the control sample. * Results statistically different from the control (strain in PBS) (ANOVA, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Adherence of bacterial strains to mucous in the presence of apple preparations. Data represent means from four repeats (±SD), where a negative percentage value indicates inhibition of adherence compared to the control sample, and a positive percentage value indicates simulation of adherence compared to the control sample. * Results statistically different from the control (strain in PBS), (ANOVA, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>PCA biplot showing the relationships between the molecular weight, prebiotic activity, and chemical composition of POS, with adherence to collagen (A—<span class="html-italic">E. coli</span> ATCC 10536, B—<span class="html-italic">S. typhimurium</span> ATCC 14028, C—<span class="html-italic">E. facealis</span> ATCC 29212, D—<span class="html-italic">L. monocytogenes</span> ATCC 19115, E—<span class="html-italic">L. plantarum</span> 0981, F—<span class="html-italic">L. brevis</span> 0984, G—<span class="html-italic">Bifidobacterium</span> ssp. 76/1/1, H—<span class="html-italic">Bifidobacterium</span> ssp. 71/1/2), adherence to mucous (I—<span class="html-italic">E. coli</span> ATCC 10536, J—<span class="html-italic">S</span>. Typhimurium ATCC 14028, K—<span class="html-italic">E. facealis</span> ATCC 29212, L—<span class="html-italic">L. monocytogenes</span> ATCC 19115, M—<span class="html-italic">L. plantarum</span> 0981, N—<span class="html-italic">L. brevis</span> 0984, O—<span class="html-italic">Bifidobacterium</span> ssp. 76/1/1, P—<span class="html-italic">Bifidobacterium</span> ssp. 71/1/2), POS fermentability (Q—<span class="html-italic">E. coli</span> ATCC 10536, R—<span class="html-italic">E. coli</span> ATCC 2739, S—<span class="html-italic">S. typhimurium</span> ATCC 14028, T—<span class="html-italic">S. typhimurium</span> ATCC 13311, U—<span class="html-italic">L. monocytogenes</span> ATCC 19115, V—<span class="html-italic">L. monocytogenes</span> ATCC 195, W—<span class="html-italic">L. plantarum</span> 0995, X—<span class="html-italic">L. plantarum</span> 0989, Y—<span class="html-italic">L. brevis</span> 0984, Z—<span class="html-italic">Bifidobacterium</span> ssp. 1, Ą—<span class="html-italic">Bifidobacterium</span> ssp. 2, Ę—<span class="html-italic">Bifidobacterium</span> ssp. 3), and chemical composition (1—xylose, 2—rhamnose, 3—galactose, 4—arabinose, 5—galacturonic acid).</p>
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29 pages, 9628 KiB  
Review
The Role of YY1 in the Regulation of LAG-3 Expression in CD8 T Cells and Immune Evasion in Cancer: Therapeutic Implications
by Adam Merenstein, Loiy Obeidat, Apostolos Zaravinos and Benjamin Bonavida
Cancers 2025, 17(1), 19; https://doi.org/10.3390/cancers17010019 - 25 Dec 2024
Viewed by 502
Abstract
The treatment of cancers with immunotherapies has yielded significant milestones in recent years. Amongst these immunotherapeutic strategies, the FDA has approved several checkpoint inhibitors (CPIs), primarily Anti-Programmed Death-1 (PD-1) and Programmed Death Ligand-1/2 (PDL-1/2) monoclonal antibodies, in the treatment of various cancers unresponsive [...] Read more.
The treatment of cancers with immunotherapies has yielded significant milestones in recent years. Amongst these immunotherapeutic strategies, the FDA has approved several checkpoint inhibitors (CPIs), primarily Anti-Programmed Death-1 (PD-1) and Programmed Death Ligand-1/2 (PDL-1/2) monoclonal antibodies, in the treatment of various cancers unresponsive to immune therapeutics. Such treatments resulted in significant clinical responses and the prolongation of survival in a subset of patients. However, not all patients responded to CPIs, due to various mechanisms of immune resistance. One such mechanism is that, in addition to PD-1 expression on CD8 T cells, other inhibitory receptors exist, such as Lymphocyte Activation Gene 3 (LAG-3), T cell Immunoglobulin Mucin 3 (TIM3), and T cell immunoreceptor with Ig and ITIM domains (TIGIT). These inhibitory receptors might be active in the presence of the above approved CPIs. Clearly, it is clinically challenging to block all such inhibitory receptors simultaneously using conventional antibodies. To circumvent this difficulty, we sought to target a potential transcription factor that may be involved in the molecular regulation of more than one inhibitory receptor. The transcription factor Yin Yang1 (YY1) was found to regulate the expression of PD-1, LAG-3, and TIM3. Therefore, we hypothesized that targeting YY1 in CD8 T cells should inhibit the expression of these receptors and, thus, prevent the inactivation of the anti-tumor CD8 T cells by these receptors, by corresponding ligands to tumor cells. This strategy should result in the prevention of immune evasion, leading to the inhibition of tumor growth. In addition, this strategy will be particularly effective in a subset of cancer patients who were unresponsive to approved CPIs. In this review, we discuss the regulation of LAG-3 by YY1 as proof of principle for the potential use of targeting YY1 as an alternative therapeutic approach to preventing the immune evasion of cancer. We present findings on the molecular regulations of both YY1 and LAG-3 expressions, the direct regulation of LAG-3 by YY1, the various approaches to targeting YY1 to evade immune evasion, and their clinical challenges. We also present bioinformatic analyses demonstrating the overexpression of LAG-3, YY1, and PD-L1 in various cancers, their associations with immune infiltrates, and the fact that when LAG-3 is hypermethylated in its promoter region it correlates with a better overall survival. Hence, targeting YY1 in CD8 T cells will result in restoring the anti-tumor immune response and tumor regression. Notably, in addition to the beneficial effects of targeting YY1 in CD8 T cells to inhibit the expression of inhibitory receptors, we also suggest targeting YY1 overexpressed in the tumor cells, which will also inhibit PD-L1 expression and other YY1-associated pro-tumorigenic activities. Full article
(This article belongs to the Special Issue Cancer Immunotherapy in Clinical and Translational Research)
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<p>YY1 gene structure. This figure provides a comprehensive depiction of the Yin Yang 1 (YY1) transcription factor, illustrating its key structural domains and functional regions. YY1 is 414 amino acids long and consists of three major domains. The transactivation domains have acidic domains of around 70 amino acids each. The histidine domains for activation of YY1 have 11 histidines in a row. The repression domains have a 32 amino acid-long Glycine Alanine Domain and a 25 amino acid-long MBTD1-binding domain. Finally, in the DNA-binding domains of YY1, there are four zinc finger domains. YY1 contributes to cancer progression and immune evasion in various ways. YY1 regulates the protein stability and expression of many different cancer-associated genes. YY1 also contributes to the upregulation or downregulation of various T cell stability and regulation processes, contributing to a much stronger immune evasion response. Prepared by BioRender, Inc. (Toronto, ON, Canada).</p>
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<p>The KIELLE domain in LAG-3. This figure depicts the structural organization of the LAG-3 protein, a key inhibitory receptor involved in the regulation of immune responses. The KIELLE domain, the CP domain, and the IgG domains. Created by BioRender, Inc.</p>
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<p>LAG-3 higher affinity for MHC-II. CD4 cells use four extracellular immunoglobulin superfamily-like domains (d1–d4). LAG-3 utilizes the extra loop with 30 amino acids in D1 to bind to MHC class II with greater affinity. Ligation of MHC class II, by antigen presenting cells or aberrantly by melanoma cells, with LAG-3 mediates an intrinsic negative inhibitory signal, in which the KIEELE motif in the cytoplasmic domain is indispensable. LAG-3 is highly glycosylated with LSECtin, expressed on melanoma cells, and Galectin-3 is expressed on stromal cells and CD8<sup>+</sup> T cells in the tumor microenvironment. This figure shows the interaction between LAG-3 and these three ligands and how it interacts with CD4 and CD8 T cells. Created by BioRender, Inc.</p>
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<p>Regulation of PD-1 and LAG-3 by YY1 in tumor-infiltrating CD8 T lymphocytes. This figure depicts p38MAPK/JNK/YY1/LAG-3-PD-1 pathway in tumor-infiltrating lymphocytes. MAP3K activation increases JNK and p38, leading to an increase in YY1 expression. This pathway, which drives YY1 expression, leads to YY1-mediated transcriptional PD-1 and LAG-3 upregulations. The anti-tumor CD8 T cells, expressing both PD-1 and LAG-3 inhibitory receptors, will bind the tumor target cells, leading to the inactivation of the CD8 T cells through their interactions with the PDL-1/2 and MHC-II, respectively. Thus, tumors escape via immune evasion and tumor growth. Created by BioRender, Inc.</p>
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<p>The expression of YY1, CD274 (PD-L1), and LAG-3 in pan-cancer using normalized and batch-corrected RSEM mRNA expression data for 14 TCGA cancer types paired with their normal tissue. (<b>a</b>) The bubble plot presents the fold change and FDR for gene expression across different cancer types, represented by bubble color and size. Rows indicate gene symbols, while columns correspond to selected cancer types. Bubble color transitions from purple to red, reflecting fold change (tumor vs. normal), and bubble size is proportional to FDR significance. (<b>b</b>,<b>c</b>) Boxplots display the expression levels of YY1, LAG-3, and CD274 between tumor and normal tissues across multiple cancers. A detailed example focusing on lung cancer is provided in panel (<b>c</b>), highlighting the differential expression patterns that may suggest varying roles in tumorigenesis.</p>
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<p>Correlation between YY1, CD274 and LAG-3 expression and immune cell infiltration in squamous cell lung carcinoma (LUSC) (<b>a</b>) and breast cancer (BRCA) (<b>b</b>). The Spearman’s test was used for correlations. The infiltrates of 24 immune cells were quantified using ImmuCellAI. Bubble size correlates with FDR significance. The black outline border indicates FDR ≤ 0.05. (<b>c</b>) Each gene’s mRNA expression was correlated with a specific immune cell’s infiltrates using scatter plots with a fitting line.</p>
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<p>Expression of YY1 and LAG-3 in five independent scRNA-seq datasets of breast cancer (GSE110686, GSE114727_10X, GSE114727_inDrop, GSE176078 and EMTAB8107). The global-scaling normalization method (‘NormalizeData’ function) in Seurat was used to scale the raw counts (UMI) in each cell to 10,000, and to log-transform the results. YY1 and LAG-3 expression levels were calculated in log2(TPM/10+1) values and displayed using UMAP.</p>
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<p>Expression of YY1 and LAG3 across different immune cells in breast cancer, using multiple GEO datasets. CD8<sup>+</sup> T cells express higher levels of YY1 compared to LAG-3.</p>
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<p>The GDC TCGA Breast Cancer (BRCA) dataset on the UCSC Xena browser was explored for LAG-3 and FGL1 methylation. (<b>a</b>) Electrophoresis result (2% agarose gel) of two methylation-specific PCR amplicons for LAG-3 and FGL1 (methylated and unmethylated DNA). (<b>b</b>) In silico analysis of LAG-3 promoter methylation using the beta values of specific markers (Illumina Human Methylation 450) shows that LAG-3 promoter is hypermethylated in breast cancer. (<b>c</b>) The Kaplan–Meier curves depict that breast cancer patients with LAG-3 hypermethylation (red curve) have better overall survival compared to those with LAG-3 hypomethylation (white curve) (<span class="html-italic">p</span> &lt; 0.05, Log-rank test). (<b>d</b>) In silico analysis shows no significant methylation levels in the promoter region of FGL1. (<b>e</b>) The Kaplan–Meier curves depict no difference in the overall survival between FLG1 hyper- and hypo-methylated breast cancer patients (<span class="html-italic">p</span> &gt; 0.05, Log-rank test). (<b>f</b>) Proposed model for LAG-3-expressing breast tumors (LAG-3 hyper-methylated), which could be targeted with Relatimab (anti-LAG-3) alone or in combination with anti-PD-1/PD-L1.</p>
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24 pages, 9420 KiB  
Article
Changes in the Phenotype and Metabolism of Peritoneal Macrophages in Mucin-2 Knockout Mice and Partial Restoration of Their Functions In Vitro After L-Fucose Treatment
by Elena L. Arzhanova, Yulia Makusheva, Elena G. Pershina, Snezhanna S. Medvedeva and Ekaterina A. Litvinova
Int. J. Mol. Sci. 2025, 26(1), 13; https://doi.org/10.3390/ijms26010013 - 24 Dec 2024
Viewed by 211
Abstract
In the development of inflammatory bowel disease (IBD), peritoneal macrophages contribute to the resident intestinal macrophage pool. Previous studies have demonstrated that oral administration of L-fucose exerts an immunomodulatory effect and repolarizes the peritoneal macrophages in vivo in mice. In this study, we [...] Read more.
In the development of inflammatory bowel disease (IBD), peritoneal macrophages contribute to the resident intestinal macrophage pool. Previous studies have demonstrated that oral administration of L-fucose exerts an immunomodulatory effect and repolarizes the peritoneal macrophages in vivo in mice. In this study, we analyzed the phenotype and metabolic profile of the peritoneal macrophages from Muc2−/− mice, as well as the effect of L-fucose on the metabolic and morphological characteristics of these macrophages in vitro. The investigation utilized flow cytometry, quantitative PCR (qPCR), measurement of the intracellular ATP and Ca2+ concentrations, an analysis of mitochondrial respiration and membrane potential, and transmission electron microscopy (TEM) for ultrastructural evaluations. The Muc2−/− mice exhibited lower intracellular ATP and Ca2+ levels in their peritoneal macrophages, a higher percentage of stellate macrophages, and an increased oxygen consumption rate (OCR), combined with a higher percentage of mitochondria displaying an abnormal ultrastructure. Additionally, there was a five-fold increase in condensed mitochondria compared to their level in C57BL/6 mice. The number of CD209+ peritoneal macrophages was reduced three-fold, while the number of M1-like cells increased two-fold in the Muc2−/− mice. L-fucose treatment enhanced ATP production and reduced the expression of the Parp1, Mt-Nd2, and Mt-Nd6 genes, which may suggest a reduction in pro-inflammatory factor production and a shift in the differentiation of peritoneal macrophages towards the M2 phenotype. Full article
(This article belongs to the Section Molecular Immunology)
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<p>Comparison of marker expression, shapes, OCR, and ECAR of peritoneal macrophages derived from C57BL/6 and <span class="html-italic">Muc2<sup>−/−</sup></span> mice. (<b>A</b>). Percentage of CD209<sup>+</sup> macrophages (M2-like type) from two mouse strains. (<b>B</b>). Percentage of CD80<sup>+</sup> macrophages (M1-like type) from two mouse strains. (<b>C</b>). Percentage of CD206<sup>+</sup> macrophages (M2-like type) from two mouse strains. (<b>D</b>). Percentage of CD86<sup>+</sup> macrophages (M1-like type) from two mouse strains. (<b>E</b>). Percentage of different shapes of macrophages from two mouse strains. (<b>F</b>). The OCRs were measured following treatment with oligomycin, FCCP, and the antimycin A/rotenone complex; (<b>G</b>). Seahorse assay scheme showing the types of respiration. (<b>H</b>). Means of different types of respiration: basal, ATP-linked, proton leak, maximal respiration, and reserve capacity; (<b>I</b>). The ECAR was measured following the treatment with oligomycin, FCCP, and the antimycin A/rotenone complex. (<b>J</b>). Means of basal glycolysis and glycolytic capacity. “C57BL/6” vs. “<span class="html-italic">Muc2<sup>−/−</sup></span>”: * <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 according to the PERMANOVA test.</p>
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<p>Ultrastructures of macrophages from the lamina propria of colons derived from C57BL/6 and <span class="html-italic">Muc2<sup>−/−</sup></span> mice. (<b>A</b>). Morphology of the macrophages from the two mouse strains. <span class="html-italic">Muc2<sup>−/−</sup></span> mice macrophages have a lot of altered mitochondria. (<b>B</b>). Mitochondria with “empties” (white arrows show the “empties”) (<b>C</b>). Different structural defects and functional types of ultrastructures in the mitochondria in the lamina propria of the colons of the <span class="html-italic">Muc2<sup>−/−</sup></span> mice, white arrows show the defects (<b>D</b>). The number of mitochondria in 1 μm<sup>2</sup> of the cytoplasm. (<b>E</b>). Percentages of mitochondria with normal and altered ultrastructures in <span class="html-italic">Muc2<sup>−/−</sup></span> (dark blue) and C57BL/6 (light blue) mice (<b>F</b>). Percentages of mitochondria with empty spaces and the number of cristae per mitochondria; “C57BL/6” vs. “<span class="html-italic">Muc2<sup>−/−</sup></span>”; * <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; <span class="html-italic">t</span>-test.</p>
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<p>Ultrastructure and mitochondrial membrane potential of peritoneal macrophages derived from C57BL/6 and <span class="html-italic">Muc2<sup>−/−</sup></span> mice. (<b>A</b>). Morphology of the peritoneal macrophages from the two mouse strains. white arrows show dilated cristae in condensed mitochondria. (<b>B</b>). Morphology of the mitochondria in the peritoneal macrophages from the two mouse strains. (<b>C</b>). Size of peritoneal macrophages and the mitochondria in them in the two mouse strains. (<b>D</b>). Percentages of mitochondria with normal and altered ultrastructures in the <span class="html-italic">Muc2<sup>−/−</sup></span> (dark blue) and C57BL/6 (light blue) mice. (<b>E</b>). A FACS graph with the percentage of peritoneal macrophages with a different mitochondrial membrane potential (ΔѰm) analyzed using JC-1 staining. (<b>F</b>). Percentages of peritoneal macrophages with a high and low mitochondrial membrane potential (ΔѰm). “C57BL/6” vs. “<span class="html-italic">Muc2<sup>−/−</sup></span>”; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">t</span>-test.</p>
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<p>Addition of fucose to the cell culture medium affected the production of cytokines, ATP and Ca<sup>2+</sup> levels, and surface and intracellular expression of CD38 of peritoneal macrophages derived from C57BL/6 and <span class="html-italic">Muc2<sup>−/−</sup></span> mice. (<b>A</b>). Inflammatory cytokine levels in cell culture medium of peritoneal macrophages from the two mouse strains with and without the addition of 0.1% L-fucose; the median, min, and max for each cytokine is presented in pg/mg protein. (<b>B</b>). ATP levels in the peritoneal macrophages of two mouse strains incubated with and without 0.1% L-fucose. (<b>C</b>). Ca<sup>2+</sup> levels in the peritoneal macrophages of the two mouse strains incubated with and without 0.1% L-fucose. (<b>D</b>). Percentage of peritoneal macrophages with surface CD38 expression from the two mouse strains incubated with and without 0.1% L-fucose. (<b>E</b>). Percentage of peritoneal macrophages with intracellular CD38 expression from the two mouse strains incubated with and without 0.1% L-fucose. “C57BL/6” vs. “<span class="html-italic">Muc2<sup>−/−</sup></span>“ and “with 0.1% L-fucose” vs. “without 0.1% L-fucose”: * <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. Two-way PERMANOVA test.</p>
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<p>Effect of fucose on the expression of the <span class="html-italic">Parp1</span>, <span class="html-italic">Parg</span>, <span class="html-italic">Il1b</span>, <span class="html-italic">Ltc4</span>, <span class="html-italic">Mt-nd2</span>, and <span class="html-italic">Mt-nd6</span> genes and on Tlr2 and Tlr4 expression on the surface of the peritoneal macrophages. (<b>A</b>). Expression of the <span class="html-italic">Parp1</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>B</b>). Expression of the <span class="html-italic">Parg</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>C</b>). Expression of the <span class="html-italic">Il1b</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>D</b>). Expression of the <span class="html-italic">Ltc4</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>E</b>). Expression of the <span class="html-italic">Mt-nd2</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>F</b>). Expression of the <span class="html-italic">Mt-nd6</span> gene in peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>G</b>). Percentage of Tlr2-positive peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. (<b>H</b>). Percentage of Tlr4-positive peritoneal macrophages from the two mouse strains incubated with and without 0.1% L-fucose. “C57BL/6” vs. “<span class="html-italic">Muc2<sup>−/−</sup></span>” and “with 0.1% L-fucose” vs. “without 0.1% L-fucose”: * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. Two-way PERMANOVA test.</p>
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22 pages, 6819 KiB  
Article
COSMC-Regulated O-Glycosylation: A Bioinformatics-Driven Biomarker Identification for Stratifying Glioblastoma Stem Cell Subtypes
by Sara Sadat Aghamiri and Rada Amin
Kinases Phosphatases 2024, 2(4), 391-412; https://doi.org/10.3390/kinasesphosphatases2040025 - 22 Dec 2024
Viewed by 429
Abstract
Glioblastoma stem cells (GSCs) are key drivers of relapse, metastasis, and therapy resistance in glioblastoma due to their adaptability and diversity, which make them challenging to target effectively. This study explores the O-glycosylation in differentiating two key GSC subtypes, CD133 and CD44. We [...] Read more.
Glioblastoma stem cells (GSCs) are key drivers of relapse, metastasis, and therapy resistance in glioblastoma due to their adaptability and diversity, which make them challenging to target effectively. This study explores the O-glycosylation in differentiating two key GSC subtypes, CD133 and CD44. We utilized the TCGA dataset of GBM and presented the reproducible bioinformatics analysis for our results. Our profiling showed enriched O-glycosylation signatures in CD44-expressing GBM cells over CD133, with Cosmc, the chaperone for core mucin-type O-glycosylation, significantly upregulated in the CD44-positive group. Moreover, Cosmc was associated with shorter progression-free intervals, suggesting its potential as an indicator of aggressive disease. High Cosmc expression also enriched immune-related pathways, including inflammatory response and antigen presentation, and was associated with presence of myeloid cells, T cells, and NK cells. Additionally, elevated Cosmc correlated with extracellular matrix (ECM) pathways and stromal cell populations, such as perivascular fibroblasts. These findings position O-glycosylation, specially, Cosmc as a promising biomarker for distinguishing GSC subclones, with relevance to immune modulation, and ECM dynamics, identifying it as a potential target for novel GBM therapies. Full article
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Figure 1
<p>COSMC is associated with the CD44 GBM stem cell marker, but not with CD133. (<b>A</b>) The O-glycosylation-related pathways enriched in CD44 (red) and CD133 (green), with a <span class="html-italic">p</span>-value shown. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) The C Venn diagram represents the shared and unique gene number among the O-linked_glycosylation_Of_Mucins in the CD44 and CD133 groups. (<b>C</b>) The log2 normalized TPM median gene expression of 8 genes in the CD133 group compared to normal brain tissue within Gepia, * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) The log2 normalized TPM median gene expression of 6 genes in the CD44 group compared to normal brain tissue within Gepia. The statistical test was performed using one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05. (<b>E</b>) Forest plot of overall survival of the significant overexpressed genes among both CD133 and CD44 groups. (<b>F</b>) Forest plot of progression-free interval of the significant overexpressed genes among both CD133 and CD44 groups. The statistical test was performed using the log rank test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>O-glycosylation core pathways in GBM. (<b>A</b>) Cosmc O-glycosylation model, GalNAc, N-acetylgalactosamine, GlcNAc, N-acetylglucosamine. (<b>B</b>) Gene expression of C1GALT1, ST3GAL1 and B3GNT3 compared in matched normal brain versus GBM. Statistical test was performed using one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) Heatmap displaying the Spearman correlation between the O-glycosylation core with CD133 and CD44. Statistical test was performed with Spearman correlation test, and the significant <span class="html-italic">p</span> value was displayed on the heatmap. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) Kaplan–Meier curve of overall and progression-free survival analysis of C1GALT1, ST3GAL1, with code color as follows: high (red) and low expression (blue). The difference between the two curves were determined by the two-sided log-rank test.</p>
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<p>COSMC is associated with mesenchymal stem-like phenotype. (<b>A</b>) Top 10 stemness-related pathways from GSEA analysis enriched in the Cosmc group. Blue and red indicate the significance −log10 (<span class="html-italic">p</span>-value) for enrichments of the pathway. (<b>B</b>) Spearman correlation between GSVA Cosmc and enrichment scores obtained from the stem cell marker sets. (<b>C</b>) Hallmark gene sets from GSEA analysis enriched in Cosmc. The upregulated and downregulated significant signatures are represented in the normalized enrichment score (NES) with a <span class="html-italic">p</span>-value shown with the code color. (<b>D</b>) Spearman correlation between Cosmc gene expression slow-cycling genes (<span class="html-italic">G0S2</span> and <span class="html-italic">CDKN1A</span>) and fast-cycling genes (<span class="html-italic">CDK2, CCNB1,</span> and <span class="html-italic">MKI67</span>), carried out by plotting log2 normalized count (norm count + 1) for each marker. <span class="html-italic">CDK2</span> (cyclin-dependent kinase 2)<span class="html-italic">, CCNB1</span> (cyclin B1)<span class="html-italic">, CDKN1A</span> (cyclin-dependent kinase inhibitor 1A), <span class="html-italic">G0S2</span> (G0/G1 switch gene 2). (<b>E</b>) Spearman correlation bubble plot of PN-GSC (black) and MES-GSC (purple) markers associated with Cosmc expression. The significance shows the correlation coefficient with a −log10 <span class="html-italic">p</span>-value.</p>
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<p>The association between COSMC and the immune microenvironment in GB. (<b>A</b>) Spearman correlation of immune and estimate score with Cosmc log2 (norm count + 1). (<b>B</b>) Top reactome and biocarta immune-related pathways for high Cosmc expression versus low expression. (<b>C</b>) Spearman correlation bubble plot of various immune cells associated with Cosmc gene expression. The significance shows the correlation coefficient with a −log10 <span class="html-italic">p</span>-value. Eo (eosinophils), Mast (mast cells), B (B cells), Neu (neutrophils), NKT (natural killer T cells), MDSC (myeloid-derived suppressor cells), Mono (monocytes), DC (dendritic cells).</p>
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<p>The association between COSMC with the stroma in GBM. (<b>A</b>) Spearman correlation test between stroma with Cosmc expression in GBM cohort. (<b>B</b>) The most significant canonical ECM signaling from GSEA for high Cosmc expression versus low expression, with a <span class="html-italic">p</span>-value shown with a code color. (<b>C</b>) Spearman correlation bubble plot of various stroma cells associated with Cosmc expression. CAFs—cancer-associated fibroblasts; P-Fb—perivascular fibroblasts, M-Fb—meningeal fibroblasts; SMCs—smooth muscle cells; ECM—extracellular matrix, high and low. The significance shows the correlation coefficient with a −log10 <span class="html-italic">p</span>-value.</p>
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<p>(<b>A</b>) Functional Cosmc in cells is essential for the proper production of complex O-glycans, which include various branched and elongated structures composed of N-acetylgalactosamine (GalNAc), galactose, sialic acid, N-acetylglucosamine (GlcNAc) and fucose (Fuc), and sometimes additional monosaccharides. These complex O-glycans play critical roles in the maintenance of cellular homeostasis. However, a dysfunctional Cosmc/C1GALT1 leads to truncated O-glycans, such as the Tn and T antigen. The accumulation of these truncated O-glycans disrupts normal cellular functions and is associated with cancer progression, immune evasion, and poor clinical outcomes. (<b>B</b>) Cosmc expression was specifically elevated in CD44-positive glioblastoma stem cells (GSCs) and was associated with a more complex tumor microenvironment, characterized by the presence of inflammatory (DCs—dendritic cells, Th—T helper cells, NKs—natural killer cells, NKTs—natural killer T cells), immunosuppressive (MDSCs—myeloid-derived suppressor cells, Treg—T regulatory) and stromal cells (CAFs—cancer-associated fibroblasts, SMCs—smooth muscle cells). This elevated expression of Cosmc not only influenced the cellular composition of the TME, but also played a key role in modulating the extracellular matrix (ECM) architecture. By affecting ECM remodeling, Cosmc might influence cell adhesion, migration, and invasion, which are critical processes in GBM progression and therapeutic resistance.</p>
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<p>Cosmc is strongly associated with CD44 across several GBM datasets. (<b>A</b>) Spearman correlation between normalized log2 gene expression of CD44 and Cosmc in the Ivy Glioblastoma Atlas Project (Ivy-GAP). (<b>B</b>) Spearman correlation between normalized log2 gene expression of CD133 and Cosmc in the Ivy Glioblastoma Atlas Project (Ivy-GAP). Statistical significance is indicated by the <span class="html-italic">p</span>-value. (<b>C</b>) Cosmc gene expression assessed in CD133<sup>−</sup> and CD133<sup>+</sup> CSCs sorted using CD133 markers from the GSE85297 dataset. The expression is displayed in transformed normalized transcript per millions (TPM). (<b>D</b>) Cosmc gene expression assessed in CD133<sup>−</sup> and CD133<sup>+</sup> GBM samples, sorted using CD133 markers from the GSE34152 dataset. (<b>E</b>) Cosmc gene expression analyzed in CD133<sup>−</sup>/CD133<sup>+</sup> and CD44<sup>−</sup>/CD44<sup>+</sup> GBM groups, stratified based on the median gene expression levels of CD133 and CD44 in the GSE77530 dataset. Statistical analysis comparing the two groups was performed using an unpaired Student’s <span class="html-italic">t</span>-test. **** <span class="html-italic">p</span> &lt; 0.0001, NS—non significant.</p>
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<p>Cosmc is enriched in stemness and mesenchymal-like phenotype across different GBM datasets. (<b>A</b>) Differential Cosmc gene expression comparing normal brain tissue (red) to GBM samples (green) in the Rembrandt cohort, using unpaired Student’s <span class="html-italic">t</span>-tests (**** <span class="html-italic">p</span> &lt; 0.001). (<b>B</b>) Differential Cosmc gene expression comparing normal brain tissue (red) to GBM samples (green) in the Gravendeel cohort, using unpaired Student’s <span class="html-italic">t</span>-tests (** <span class="html-italic">p</span> &lt; 0.01). (<b>C</b>) Heatmap displaying the Spearman correlation between the Cosmc with GBM stem cell markers (<span class="html-italic">NRF2, ITGA, CXCR4, uPA, uPAR, PPARG</span> and <span class="html-italic">RUNX1</span>) in the Rembrandt and Gravendeel cohorts. A statistical test was performed with the Spearman correlation test, and the significant <span class="html-italic">p</span> value is displayed on the heatmap. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Heatmap displaying the Spearman correlation between the Cosmc with GBM slow-cycling genes (<span class="html-italic">G0S2</span> and <span class="html-italic">CDKN1A</span>) and with fast-cycling genes (<span class="html-italic">CDK2, CCNB1,</span> and <span class="html-italic">MKI67</span>) in the Rembrandt and Gravendeel cohorts. A statistical test was performed with the Spearman correlation test, and the significant <span class="html-italic">p</span> value is displayed on the heatmap. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. (<b>E</b>) Cosmc gene expression in proneural (PN) and mesenchymal (MES) subtypes in Rembrandt and Gravendeel cohorts. Statistical analysis comparing the two groups was performed using an unpaired Student’s <span class="html-italic">t</span>-test. **** <span class="html-italic">p</span> &lt; 0.0001, * <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 8172 KiB  
Article
Amelioration of LPS-Induced Jejunum Injury and Mucus Barrier Damage in Mice by IgY Embedded in W/O/W Emulsion
by Zhaohui Wang, Ruihua Ye, Shidi Zhang, Chuanming Liu, Ke Chen, Kongdi Zhu, Pengjie Wang, Fuqing Wang and Jiaqiang Huang
Foods 2024, 13(24), 4138; https://doi.org/10.3390/foods13244138 - 20 Dec 2024
Viewed by 313
Abstract
Chicken yolk immunoglobulin (IgY) is a natural immunologically active antibody extracted from egg yolk and can be used as a natural dietary supplement for the treatment of inflammation and damage to the intestines. In our study, IgY was embedded in a double emulsion [...] Read more.
Chicken yolk immunoglobulin (IgY) is a natural immunologically active antibody extracted from egg yolk and can be used as a natural dietary supplement for the treatment of inflammation and damage to the intestines. In our study, IgY was embedded in a double emulsion (W/O/W; DE) to explore the therapeutic effect of the embedded IgY on Lipopolysaccharide (LPS)-induced jejunal injury in mice. The results showed that W/O/W-embedded IgY as a dietary supplement (IgY + DE) attenuated LPS-induced damage to mouse small intestinal structures and protected the integrity of the jejunal mucosal barrier. IgY + DE increased the amount of related transcription factors (Math1, Spdef, Elf3, and Klf4) and promoted thrush cell differentiation. IgY + DE ameliorated LPS-induced reduction in mucin quantity and markers. It promoted the expression of Muc1 and Muc2 and increased the mRNA expression levels of Muc1, Muc2, Muc3, Muc4, Muc13, and Agr2 (p < 0.05). IgY + DE increased the expression of several glycosyltransferases involved in mucin glycosylation. IgY + DE also neutralized the LPS attack on the expression of jejunal inflammatory factors IL-1β, IL-6, IL-4, and TNF-α. In conclusion, the IgY-embedded double emulsion can be used as a dietary supplement for immunotherapy to prevent LPS-induced jejunal injury in mice. Full article
(This article belongs to the Special Issue Bioavailability and Health Benefits of Bioactive Compounds in Foods)
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Figure 1
<p>Experimental research design. After a 7-day acclimatization period, the mice were treated by gavage. Gavage was performed daily from 0 to 14 d. The mice were treated by gavage at the end of the 7-day acclimatization period. At the end of the gavage, mice were injected intraperitoneally at 15 d. The mice were then treated with a daily gavage treatment from 0 to 14 d.</p>
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<p>IgY + DE alleviates LPS-induced structural damage of small intestinal tissues. (<b>a</b>) H&amp;E staining of duodenum, jejunum, and ileum (n = 5) (scale bar: 100 μm). (<b>b</b>) Villus heights of duodenum (n = 7). (<b>c</b>) Villus heights of jejunum (n = 7). (<b>d</b>) Villus heights of ileum (n = 7). (<b>e</b>) Crypt depth of duodenum (n = 7). (<b>f</b>) Crypt depth of jejunum (n = 7). (<b>g</b>) Crypt depth of ileum (n = 7). (<b>h</b>) V/C ratio in duodenum (n = 7). (<b>i</b>) V/C ratio in jejunum (n = 7). (<b>j</b>) V/C ratio in ileum (n = 7). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; ns: represents no significance between the values. Control: 0.9% NaCl gavage; LPS: 0.9% NaCl gavage; IgY: unembedded IgY gavage; DE: W/O/W gavage; IgY + DE: W/O/W gavage with embedded IgY.</p>
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<p>IgY + DE increased associated transcription factors and goblet cell differentiation. (<b>a</b>) AB-PAS staining of the jejunum (scale: 100 µm) (n = 5) (red arrows sign goblet cells.). (<b>b</b>) The number of goblet cells expressed as positive cells per villus in jejunum. (<b>c</b>–<b>g</b>) Relative mRNA expression of transcription factors involved in goblet cell differentiation in the jejunum. (<b>c</b>) Math1 mRNA expression (n = 5). (<b>d</b>) Hes1 mRNA expression (n = 5). (<b>e</b>) Spdef mRNA expression (n = 5). (<b>f</b>) Elf3 mRNA expression (n = 5). (<b>g</b>) Klf4 mRNA expression (n = 5). Atonal bHLH transcription factor 1 (Math1), his family bHLH transcription factor 1 (Hes1), SAM pointed domain containing ETS transcription factor (Spdef), E74-like ETS transcription factor 3 (Elf3), kruppel-like factor 4 (Klf4). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; ns: represents no significance between the values. Control: 0.9% NaCl gavage; LPS: 0.9% NaCl gavage; IgY: unembedded IgY gavage; DE: W/O/W gavage; IgY + DE: W/O/W gavage with embedded IgY.</p>
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<p>IgY + DE ameliorated the LPS-induced reduction in mucin number and markers. (<b>a</b>) Immunohistochemical staining of Muc1 in jejunum sections (scale bar 100 µm) (n = 5) (red arrows indicate Muc1). (<b>b</b>) Immunohistochemical staining of Muc2 in jejunum sections (scale bar 100 µm) (n = 5) (red arrows indicate Muc2). (<b>c</b>) IOD values of jejunum Muc1-positive cells. (<b>d</b>) IOD values of jejunum Muc2-positive cells. The IOD value refers to the integral optical density, which is a numerical indicator of the brightness or intensity of a specific stained area in a tissue section quantified by image analysis techniques. (<b>e</b>–<b>j</b>) Relative mRNA expression of markers involved in mucin production in the jejunum. (<b>e</b>) Muc2 mRNA expression (n = 5). (<b>f</b>) Agr2 mRNA expression (n = 5). (<b>g</b>) Muc1 mRNA expression (n = 5). (<b>h</b>) Muc3 mRNA expression (n = 5). (<b>i</b>) Muc4 mRNA expression (n = 5). (<b>j</b>) Muc13 mRNA expression (n = 5). Anterior gradient 2 (Agr2); mucin 2 (Muc2); mucin 1/3/4/13 (Muc1, Muc3, Muc4, and Muc13). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; ns: represents no significance between the values. Control: 0.9% NaCl gavage; LPS: 0.9% NaCl gavage; IgY: unembedded IgY gavage; DE: W/O/W gavage; IgY + DE: W/O/W gavage with embedded IgY.</p>
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<p>IgY + DE increased the expression of glycosyltransferases involved in mucin glycosylation. (<b>a</b>) Gcnt1 mRNA expression (n = 5). (<b>b</b>) Gcnt4 mRNA expression (n = 5). (<b>c</b>) C1galt1 mRNA expression (n = 5). (<b>d</b>) C1galt1c1 mRNA expression (n = 5). (<b>e</b>) Fut1 mRNA expression (n = 5). (<b>f</b>) Fut2 mRNA expression (n = 5). (<b>g</b>) Fut8 mRNA expression (n = 5). (<b>h</b>) St3gal1 mRNA expression (n = 5). (<b>i</b>) St3gal4 mRNA expression (n = 5). (<b>j</b>) St3gal6 mRNA expression (n = 5). (<b>k</b>) St6galnac2 mRNA expression (n = 5). (<b>a</b>–<b>d</b>) glucosaminyl (N-acetyl) transferase 1 (Gcnt1), glucosaminyl (N-acetyl) transferase 4 (Gcnt4), core 1 synthase, glycoproteinN-acetylgalactosamine 3-beta-galactosylt- ansferase 1 (C1galt1), C1GALT1 specific chaperone 1 (C1galt1c1), (<b>e</b>–<b>g</b>) fucosyltransferase 1/2/8 (Fut1, Fut2, Fut8), (<b>h</b>–<b>k</b>) ST3 beta-galactoside alpha-2,3-sialyltransferase 1/3/4/6 (St3gal1, St3gal3, St4gal4, St3gal6), ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 2 (St6galnac2). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; ns: represents no significance between the values. Control: 0.9% NaCl gavage; LPS: 0.9% NaCl gavage; IgY: unembedded IgY gavage; DE: W/O/W gavage; IgY + DE: W/O/W gavage with embedded IgY.</p>
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<p>IgY + DE inhibited LPS-induced expression of inflammatory factors in mouse jejunum. (<b>a</b>) IL-6 mRNA expression (n = 5). (<b>b</b>) IL-10 mRNA expression (n = 5). (<b>c</b>) IL-1b mRNA expression (n = 5). (<b>d</b>) TNF-a mRNA expression (n = 5). (<b>e</b>) IL-17a mRNA expression (n = 5). (<b>f</b>) Expression of IL-1β inflammatory factor. (n = 5). (<b>g</b>) Expression of TNF-α inflammatory factor. (n = 5). (<b>h</b>) Expression of IL-6 inflammatory factor. (n = 5). (<b>i</b>) Expression of IL-10 inflammatory factor. (n = 5). (<b>j</b>) Expression of IL-4 inflammatory factor. (n = 5) * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001. Control: 0.9% NaCl gavage; LPS: 0.9% NaCl gavage; IgY: unembedded IgY gavage; DE: W/O/W gavage; IgY + DE: W/O/W gavage with embedded IgY.</p>
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16 pages, 2497 KiB  
Article
Expression Levels of MUC5AC and MUC5B in Airway Goblet Cells Are Associated with Traits of COPD and Progression of Chronic Airflow Limitation
by Terezia Pincikova, Heta Merikallio, Ioanna Kotortsi, Reza Karimi, Chuan-Xing Li, Elisa Lappi-Blanco, Sara K. Lindén, Médea Padra, Åsa M. Wheelock, Sven Nyrén, Carl Magnus Sköld and Riitta L. Kaarteenaho
Int. J. Mol. Sci. 2024, 25(24), 13653; https://doi.org/10.3390/ijms252413653 - 20 Dec 2024
Viewed by 535
Abstract
Mucins 5AC (MUC5AC) and 5B (MUC5B) are the major mucins providing the organizing framework for the airway’s mucus gel. We retrieved bronchial mucosal biopsies and bronchial wash (BW) samples through bronchoscopy from patients with chronic obstructive pulmonary disease (n = 38), healthy [...] Read more.
Mucins 5AC (MUC5AC) and 5B (MUC5B) are the major mucins providing the organizing framework for the airway’s mucus gel. We retrieved bronchial mucosal biopsies and bronchial wash (BW) samples through bronchoscopy from patients with chronic obstructive pulmonary disease (n = 38), healthy never-smokers (n = 40), and smokers with normal lung function (n = 40). The expression of MUC5AC and MUC5B was assessed immunohistochemically. The mucin concentrations in BW were determined using the slot-blot technique. The immunohistochemical expression of MUC5AC and MUC5B was localized to goblet cells and submucosal glands. Smokers had higher MUC5AC and lower MUC5B goblet cell expression and higher concentrations of soluble MUC5AC in BW than never-smokers. The MUC5B expression in goblet cells correlated positively with expiratory air flows, diffusing capacity, and the dyspnoea score. Chronic bronchitis, emphysema, and the progression of chronic airflow limitation during a median follow-up time of 8.4 years were associated with higher MUC5AC and lower MUC5B expression in goblet cells. Sustainers, slow progressors, and rapid progressors of airflow obstruction differed in their MUC5B expression at baseline. Emphysema and bronchial wall thickening on CT at a follow-up visit were associated with lower MUC5B expression at baseline. Our findings strengthen the hypothesis that MUC5AC and MUC5B are yet another contributing factor to smoking-associated lung disease progression. Full article
(This article belongs to the Special Issue Biomarkers of Lung Disorders)
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Graphical abstract

Graphical abstract
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<p>Representative images of the immunohistochemical staining for MUC5AC and MUC5B in bronchial biopsy samples retrieved from large airways. Scores of the expression were assigned as negative (0), faint (1), moderate (2), strong (3), or very strong (4). MUC5AC expression in goblet cells in a never-smoker with normal lung function (<b>A</b>), a smoker with normal lung function (<b>B</b>), and a smoker with COPD (<b>C</b>). MUC5B expression in goblet cells in a never-smoker (<b>D</b>), a smoker with normal lung function (<b>E</b>), and a smoker with COPD (<b>F</b>). The length of the scale bar in the immunohistochemical images is 50 µm. Black arrows (→) show goblet cells.</p>
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<p>MUC5AC expression (<b>A</b>) and MUC5B expression (<b>B</b>) in goblet cells in never-smokers, smokers with normal lung function, smokers with COPD, and ex-smokers with COPD. MUC5AC concentration (<b>C</b>) and MUC5B concentration (<b>D</b>) in BW in never-smokers, smokers with normal lung function, smokers with COPD, and ex-smokers with COPD. Kruskal–Wallis Test was used in all. Bars represent the median with interquartile range.</p>
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<p>Associations between smoking exposure and the immunohistochemical expression for MUC5AC and MUC5B. Association between current cigarette consumption and MUC5AC and MUC5B immunohistochemical expression in goblet cells in smokers with normal lung function and smokers with COPD (<b>A</b>,<b>B</b>). Association between smoking history (pack-years) and MUC5AC expression (<b>C</b>) and MUC5B expression (<b>D</b>) in goblet cells in smokers with normal lung function, smokers with COPD, and ex-smokers with COPD. Spearman correlation analyses in all.</p>
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<p>Differences in MUC5AC expression, MUC5B expression, and MUC5AC concentration in BW in subjects with or without emphysema (<b>A</b>–<b>C</b>) and in subjects with or without chronic bronchitis (<b>D</b>–<b>F</b>) at baseline. The immunohistochemical MUC5AC expression (<b>A</b>,<b>D</b>) and MUC5B expression (<b>B</b>,<b>E</b>) in goblet cells, and MUC5AC concentration in BW samples (<b>C</b>,<b>F</b>). Mann–Whitney U Test in all. Bars represent the median with interquartile range.</p>
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<p>Association between change in FEV1/FVC ratio per year in the follow-up study cohort (<span class="html-italic">n</span> = 74) and the immunohistochemical expression of MUC5AC (<b>A</b>) and MUC5B (<b>B</b>) assessed in biopsies of large airway mucosa retrieved at baseline. The immunohistochemical MUC5B expression (<b>C</b>) in goblet cells at baseline, assessed in sustainers, slow progressors, and rapid progressors. Sustainers, slow progressors, and rapid progressors were defined as subjects with a change in the FEV1/FVC ratio during the follow-up time as above the upper quartile, between the lower and the upper quartile, and below the lower quartile, respectively. Spearman correlation analyses in (<b>A</b>,<b>B</b>). Kruskal–Wallis Test in (<b>C</b>). Bars represent the median with interquartile range.</p>
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<p>The immunohistochemical expression of MUC5B in goblet cells (<b>A</b>) and the concentration of soluble MUC5AC in BW (<b>B</b>) at baseline in subjects who had emphysema on CT scan at the end of follow-up compared with subjects who did not have emphysema on CT scan at the end of follow-up. The immunohistochemical expression of MUC5B in goblet cells (<b>C</b>) at baseline in subjects who had signs of bronchial wall thickening on CT scan at the end of follow-up compared with subjects who did not have signs of bronchial wall thickening on CT scan at the end of follow-up. Mann–Whitney U Test in all. Bars represent the median with interquartile range.</p>
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