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17 pages, 7777 KiB  
Article
The Nephroprotective Effect of Punica granatum Peel Extract on LPS-Induced Acute Kidney Injury
by Sena Sahin Aktura, Kazim Sahin, Levent Tumkaya, Tolga Mercantepe, Atilla Topcu, Esra Pinarbas and Zihni Acar Yazici
Life 2024, 14(10), 1316; https://doi.org/10.3390/life14101316 - 16 Oct 2024
Abstract
Sepsis is an exaggerated immune response resulting from systemic inflammation, which can damage tissues and organs. Acute kidney injury has been detected in at least one-third of patients with sepsis. Sepsis-associated acute kidney injury increases the risk of a secondary infection. Rapid diagnosis [...] Read more.
Sepsis is an exaggerated immune response resulting from systemic inflammation, which can damage tissues and organs. Acute kidney injury has been detected in at least one-third of patients with sepsis. Sepsis-associated acute kidney injury increases the risk of a secondary infection. Rapid diagnosis and appropriate initiation of antibiotics can significantly reduce mortality and morbidity. However, microorganisms are known to develop resistance to antibiotics. Estimations indicate that the annual casualties caused by microbial resistance will surpass cancer fatalities by 2050. The prevalence of bacterial infections and their growing antibiotic resistance has brought immediate attention to the search for novel treatments. Plant-derived supplements contain numerous bioactive components with therapeutic potential against a variety of conditions, including infections. Punica granatum peel is rich in phenolic compounds. The purpose of this study was to determine the anti-inflammatory and anti-bacterial properties of P. granatum peel extract (PGPE) on lipopolysaccharide (LPS)-induced acute kidney injury. Experimental groups were Control, LPS (10 mg/kg LPS, intraperitoneally), PGPE100, and PGPE300 (100 and 300 mg/mL PGPE via oral gavage, respectively, for 7 days). According to biochemical results, serum blood urea nitrogen (BUN), creatinine (Cr) and C-reactive protein (CRP), kidney tissue thiobarbituric acid reactive substances (TBARS), and reduced glutathione (GSH) levels significantly decreased in the PGPE groups compared to the LPS group. Histopathological and immunohistochemical findings revealed that toll-like receptor 4 (TLR4) level and nuclear factor kappa B (NF-κB) expression increased in the LPS group compared to the Control group. In addition, the anti-Gram-negative activity showed a dose-dependent effect on Acinetobacter baumannii, Escherichia coli, and Pseudomonas aeruginosa with the agar well diffusion method and the minimal inhibitory concentration (MIC). The MIC value was remarkable, especially on A. baumannii. We conclude that PGPE has the potential to generate desirable anti-bacterial and anti-inflammatory effects on LPS-induced acute kidney injury in rats. Full article
(This article belongs to the Special Issue Bioactive Natural Compounds: Therapeutic Insights and Applications)
29 pages, 7405 KiB  
Article
Immunological Strategies in Gastric Cancer: How Toll-like Receptors 2, -3, -4, and -9 on Monocytes and Dendritic Cells Depend on Patient Factors?
by Marek Kos, Krzysztof Bojarski, Paulina Mertowska, Sebastian Mertowski, Piotr Tomaka, Łukasz Dziki and Ewelina Grywalska
Cells 2024, 13(20), 1708; https://doi.org/10.3390/cells13201708 - 16 Oct 2024
Abstract
(1) Introduction: Toll-like receptors (TLRs) are key in immune response by recognizing pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). In gastric cancer (GC), TLR2, TLR3, TLR4, and TLR9 are crucial for modulating immune response and tumor progression. (2) Objective: This study [...] Read more.
(1) Introduction: Toll-like receptors (TLRs) are key in immune response by recognizing pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). In gastric cancer (GC), TLR2, TLR3, TLR4, and TLR9 are crucial for modulating immune response and tumor progression. (2) Objective: This study aimed to assess the percentage of dendritic cells and monocytes expressing TLR2, TLR3, TLR4, and TLR9, along with the concentration of their soluble forms in the serum of GC patients compared to healthy volunteers. Factors such as disease stage, tumor type, age, and gender were also analyzed. (3) Materials and Methods: Blood samples from newly diagnosed GC patients and healthy controls were immunophenotyped using flow cytometry to assess TLR expression on dendritic cell subpopulations and monocytes. Serum-soluble TLRs were measured by ELISA. Statistical analysis considered clinical variables such as tumor type, stage, age, and gender. (4) Results: TLR expression was significantly higher in GC patients, except for TLR3 on classical monocytes. Soluble forms of all TLRs were elevated in GC patients, with significant differences based on disease stage but not tumor type, except for serum TLR2, TLR4, and TLR9. (5) Conclusions: Elevated TLR expression and soluble TLR levels in GC patients suggest a role in tumor pathogenesis and progression, offering potential biomarkers and therapeutic targets. Full article
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Figure 1

Figure 1
<p>Graphical representation of the characteristics of GC patients and healthy volunteers included in the study. (<b>A</b>) illustrates the distribution of GC patients by disease stage at diagnosis; (<b>B</b>) shows the division of patients by histopathological type of GC: intestinal type and diffuse type; (<b>C</b>) presents the gender distribution in both the GC patient group and the healthy control group; and (<b>D</b>) shows the age diversity of the recruited GC patients and healthy volunteers, showing the full age range of the study subjects. Abbreviations: GC, gastric cancer; HV, healthy volunteers.</p>
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<p>Comparison of the obtained results of the assessment of the percentage of the studied TLRs on dendritic cell subpopulations between patients in the individual stages of the disease. (<b>A</b>–<b>D</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on BDCA-1 dendritic cells; (<b>E</b>–<b>H</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on BDCA-2 dendritic cells. Statistically significant differences between the individual stages are marked with letters. To facilitate interpretation, the appropriate GC stages are marked with individual colors (green—stage I; blue—stage II; purple—stage III; pink—stage IV).</p>
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<p>Comparison of the obtained results of the concentration of soluble forms of the studied TLRs in serum between patients in individual stages of the disease. (<b>A</b>–<b>D</b>) Analysis of serum concentrations of sTLR-2, sTLR-3, sTLR-4, and sTLR-9. Statistically significant differences between individual stages are marked with letters. To facilitate interpretation, the appropriate GC stages are marked with individual colors (green—stage I; blue—stage II; purple—stage III; pink—stage IV).</p>
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<p>Comparison of the obtained results of the assessment of the percentage of the studied TLRs on monocyte subpopulations between patients in the individual stages of the disease. (<b>A</b>–<b>D</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on classical monocytes; (<b>E</b>–<b>H</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on intermediate monocytes; (<b>I</b>–<b>L</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on non-classical monocytes. Statistically significant differences between individual stages are marked with letters. To facilitate interpretation, the appropriate GC stages are marked with individual colors (green—stage I; blue—stage II; purple—stage III; pink—stage IV).</p>
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<p>Comparison of the obtained results of the assessment of the percentage of the studied TLRs on dendritic cell subpopulations between patients depending on the patient’s gender. (<b>A</b>–<b>D</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on BDCA-1 DCs; (<b>E</b>–H) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on BDCA-2 dendritic cells. Statistically significant differences between individual stages are marked with letters. To facilitate interpretation, the respective sexes are marked with individual colors (pink—women with GC; navy blue—men with GC; purple—women HV; light blue—men HV).</p>
Full article ">Figure 6
<p>Comparison of the obtained results of the assessment of the percentage of the studied TLRs on monocyte subpopulations between patients depending on the patient’s gender. (<b>A</b>–<b>D</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on classical monocytes; (<b>E</b>–H Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on intermediate monocytes; (<b>I</b>–<b>L</b>) Analysis of the percentage of TLR-2, TLR-3, TLR-4, and TLR-9 on non-classical monocytes. Statistically significant differences between individual stages are marked with letters. To facilitate interpretation, the respective genders are marked with individual colors (pink—women with GC; navy blue—men with GC; purple—women HV; light blue—men HV).</p>
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<p>Comparison of the obtained results of the concentration of soluble forms of the studied TLRs in serum between patients, taking into account differences in gender. (<b>A</b>–<b>D</b>) Analysis of serum concentrations of sTLR-2, sTLR-3, sTLR-4, and sTLR-9. Statistically significant differences between individual stages are marked with letters. To facilitate interpretation, individual colors indicate the appropriate genders (pink—women with GC; navy blue—men with GC; purple—women HV; light blue—men HV).</p>
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<p>Schematic representation of the Spearman rank correlation of the studied parameters in patients with newly diagnosed GC. For ease of interpretation, negative correlations are marked in red, while positive correlations are marked in blue. The intensity of the coloring of individual pairs indicates the strength of correlations.</p>
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<p>Graphical representation of selected ROC curves depending on the disease stage in GC patients. (<b>A</b>–<b>D</b>) ROC curves presenting a comparison of the sensitivity and specificity of the tested TLRs on BDCA-1; (<b>E</b>–<b>H</b>) ROC curves presenting a comparison of the sensitivity and specificity of the tested concentrations of soluble forms of TLRs in the serum of patients.</p>
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<p>Graphical representation of selected ROC curves depending on the sex of recruited patients. (<b>A</b>–<b>D</b>) ROC curves presenting a comparison of sensitivity and specificity of the tested TLRs on BDCA-1; (<b>E</b>–<b>H</b>) ROC curves presenting a comparison of sensitivity and specificity of the tested concentrations of soluble forms of TLRs in the serum of patients.</p>
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<p>Graphical representation of selected ROC curves depending on the age of recruited patients. (<b>A</b>–<b>D</b>) ROC curves presenting a comparison of sensitivity and specificity of the tested TLRs on BDCA-1; (<b>E</b>–<b>H</b>) ROC curves presenting a comparison of sensitivity and specificity of the tested concentrations of soluble forms of TLRs in the serum of patients.</p>
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17 pages, 3785 KiB  
Article
Dual Functionality of Papaya Leaf Extracts: Anti-Coronavirus Activity and Anti-Inflammation Mechanism
by Yujia Cao, Kah-Man Lai, Kuo-Chang Fu, Chien-Liang Kuo, Yee-Joo Tan, Liangli (Lucy) Yu and Dejian Huang
Foods 2024, 13(20), 3274; https://doi.org/10.3390/foods13203274 (registering DOI) - 16 Oct 2024
Viewed by 259
Abstract
Papaya leaves have been used as food and traditional herbs for the treatment of cancer, diabetes, asthma, and virus infections, but the active principle has not been understood. We hypothesized that the anti-inflammatory activity could be the predominant underlying principle. To test this, [...] Read more.
Papaya leaves have been used as food and traditional herbs for the treatment of cancer, diabetes, asthma, and virus infections, but the active principle has not been understood. We hypothesized that the anti-inflammatory activity could be the predominant underlying principle. To test this, we extracted papaya leaf juice with different organic solvents and found that the ethyl acetate (EA) fraction showed the most outstanding anti-inflammatory activity by suppressing the production of nitric oxide (NO, IC50 = 24.94 ± 2.4 μg/mL) and the expression of pro-inflammatory enzymes, such as inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX-2), and cytokines including interleukins (IL-1β and IL-6), and a tumor necrosis factor (TNF-α) in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Transcriptomic analysis and Western blot results revealed its anti-inflammatory mechanisms were through the MAPK signaling pathway by inhibiting the phosphorylation of ERK1/2, JNKs, and p38 and the prevention of the cell surface expression of TLR4. Furthermore, we discovered that the EA fraction could inhibit the replication of alpha-coronavirus (HCoV-229E) and beta-coronavirus (HCoV-OC43 and SARS-CoV-2) and might be able to prevent cytokine storms caused by the coronavirus infection. From HPLC-QTOF-MS data, we found that the predominant phytochemicals that existed in the EA fraction were quercetin and kaempferol glycosides and carpaine. Counter-intuitively, further fractionation resulted in a loss of activity, suggesting that the synergistic effect of different components in the EA fraction contribute to the overall potent activity. Taken together, our results provide preliminary evidence for papaya leaf as a potential anti-inflammatory and anti-coronavirus agent, warranting further study for its use for human health promotion. Full article
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Graphical abstract

Graphical abstract
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<p>The inhibitory effects of papaya leaf juice extract (PLJE) on NO production in LPS-induced RAW 264.7 cells (<b>A</b>). Cytotoxicity of PLJE (<b>B</b>). Data points and bar represent arithmetic means ± SD. ns, not significant. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 compared to DMSO or control group.</p>
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<p>Suppressive effects of fractions extracted from PLJE on NO production in LPS-induced RAW 264.7 cells (<b>A</b>). Cell viability of RAW 264.7 cells treated with five fractions (<b>B</b>). IC<sub>50</sub> of EA fraction on NO production in LPS-induced RAW 264.7 cells (<b>C</b>). Data points and bar represent arithmetic means ± SD. ns, not significant. ** <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 compared to DMSO group, <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; 0.0001 compared within group.</p>
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<p>The inhibitory effects of EA fraction on inflammation-related protein expression in LPS-stimulated RAW 264.7 models (<b>A</b>). The expression levels of iNOS (<b>B</b>), COX-2 (<b>C</b>), and TLR4 (<b>D</b>) were determined by Western blot. The mRNA expression levels of IL-1β (<b>E</b>), IL-6 (<b>F</b>), and TNF-α (<b>G</b>) were tested by qRT-PCR. Data points and bar represent arithmetic means ± SD. ** <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 compared to DMSO group.</p>
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<p>GO (<b>A</b>) and KEGG (<b>B</b>) enrichment scatter plots of EA (25 μg/mL) vs. DMSO group. GeneRatio is the ratio of the number of DEGs annotated to the GO or KEGG term to the total number of DEGs. The size of the dot represents the number of genes annotated to the terms.</p>
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<p>Effects of EA fraction on MAPK pathway in LPS-induced RAW 264.7 cell (<b>A</b>). Suppressive effects of EA fraction on the LPS-induced phosphorylation ratio of ERK1/2 (<b>B</b>), JNK (<b>C</b>), and p38 (<b>D</b>). Data points and bar represent arithmetic means ± SD. ** <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 compared to DMSO group.</p>
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<p>Schematic diagram of potential contribution of papaya leaves in LPS-induced signaling pathways.</p>
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<p>Plaque reduction neutralization tests (PRNT) of EA fraction and nirmatrelvir against infectious HCoVs-OC43 (<b>A</b>), HCoV-229E (<b>B</b>), and SARS-CoV-2 (<b>C</b>). Data points and bar represent arithmetic shown which are the mean ± SD of at least two independent tests performed. ns, not significant. *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 compared between indicated groups.</p>
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20 pages, 3681 KiB  
Article
The Effect of Lower Limb Combined Neuromuscular Electrical Stimulation on Skeletal Muscle Cross-Sectional Area and Inflammatory Signaling
by Amal Alharbi, Jia Li, Erika Womack, Matthew Farrow and Ceren Yarar-Fisher
Int. J. Mol. Sci. 2024, 25(20), 11095; https://doi.org/10.3390/ijms252011095 (registering DOI) - 16 Oct 2024
Viewed by 202
Abstract
In individuals with a spinal cord injury (SCI), rapid skeletal muscle atrophy and metabolic dysfunction pose profound rehabilitation challenges, often resulting in substantial loss of muscle mass and function. This study evaluates the effect of combined neuromuscular electrical stimulation (Comb-NMES) on skeletal muscle [...] Read more.
In individuals with a spinal cord injury (SCI), rapid skeletal muscle atrophy and metabolic dysfunction pose profound rehabilitation challenges, often resulting in substantial loss of muscle mass and function. This study evaluates the effect of combined neuromuscular electrical stimulation (Comb-NMES) on skeletal muscle cross-sectional area (CSA) and inflammatory signaling within the acute phase of SCI. We applied a novel Comb-NMES regimen, integrating both high-frequency resistance and low-frequency aerobic protocols on the vastus lateralis muscle, to participants early post-SCI. Muscle biopsies were analyzed for CSA and inflammatory markers pre- and post-intervention. The results suggest a potential preservation of muscle CSA in the Comb-NMES group compared to a control group. Inflammatory signaling proteins such as TLR4 and Atrogin-1 were downregulated, whereas markers associated with muscle repair and growth were modulated beneficially in the Comb-NMES group. The study’s findings suggest that early application of Comb-NMES post-SCI may attenuate inflammatory pathways linked to muscle atrophy and promote muscle repair. However, the small sample size and variability in injury characteristics emphasize the need for further research to corroborate these results across a more diverse and extensive SCI population. Full article
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Figure 1

Figure 1
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
Full article ">Figure 1 Cont.
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
Full article ">Figure 1 Cont.
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
Full article ">Figure 1 Cont.
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
Full article ">Figure 1 Cont.
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
Full article ">Figure 1 Cont.
<p>Normalized total protein levels for skeletal muscle inflammatory proteins in response to Comb-NMES vs. control. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05). Data are presented as means ± SD. Statistical significance was determined using a linear mixed model to assess group–time interactions, followed by pairwise post hoc comparisons using the Tukey–Kramer method. The model assumptions, including homogeneity of variance and normal distribution of residuals, were verified through diagnostic plots. Significance was set at <span class="html-italic">p</span> &lt; 0.05; toll-like receptor 4, TLR4; Janus kinase 1, JAK1; nuclear factor kappa-light-chain-enhancer of activated B cells, NF-kB; ribosomal protein S6 kinase, RS6K; myogenic differentiation 1, MyoD1; tumor necrosis factor alpha receptor 1, TNF-R1; tumor necrosis factor alpha, TNF-α; Interleukin 6 receptor, IL-6R; Interleukin 1 beta, IL-1β; Interleukin 1 receptor antagonist, IL-1RA; tumor necrosis factor receptor (TNFR)-associated factor 6, TRAF6; signal transducer and activator of transcription 3, STAT3; Interleukin 6, IL-6.</p>
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<p>Myofiber cross-sectional area (CSA) in response to the Comb-NMES and control groups. Data are means ± SD. Group × time interactions were evaluated using a linear mixed-effects model. Post hoc Tukey–Kramer tests were performed to determine specific differences between and within groups. Residuals were checked to ensure normal distribution and variance homogeneity. Statistical significance was defined as <span class="html-italic">p</span> &lt; 0.05. * Statistically significant changes within the group (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>A training program for a person with a complete motor SCI. (<b>A</b>) As a component of Dudley’s training (Comb-NMES), every session comprised four sets of ten actions. Initially, during the first two sessions, the individual completed four sets of 10 repetitions without introducing any additional weight. Once the person achieved 40 repetitions of full knee extension within a training session, the weights were gradually increased by 1 lb. Eventually, after completing 12 rounds, the individual was able to lift 5 pounds. (<b>B</b>) In the twitch training regimen, the exercise program commenced with 10 min of twitch activation at a frequency of 2 Hz. Over the course of the first week, this duration was gradually extended until each session lasted 30 min, with the frequency increased to 6 Hz [<a href="#B16-ijms-25-11095" class="html-bibr">16</a>].</p>
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11 pages, 2054 KiB  
Article
Variants rs3804099 and rs3804100 in the TLR2 Gene Induce Different Profiles of TLR-2 Expression and Cytokines in Response to Spike of SARS-CoV-2
by Julio Flores-González, Zurisadai Monroy-Rodríguez, Ramcés Falfán-Valencia, Ivette Buendía-Roldán, Ingrid Fricke-Galindo, Rafael Hernández-Zenteno, Ricardo Herrera-Sicairos, Leslie Chávez-Galán and Gloria Pérez-Rubio
Int. J. Mol. Sci. 2024, 25(20), 11063; https://doi.org/10.3390/ijms252011063 (registering DOI) - 15 Oct 2024
Viewed by 350
Abstract
The present study aimed to identify in patients with severe COVID-19 and acute respiratory distress syndrome (ARDS) the association between rs3804099 and rs3804100 (TLR2) and evaluate the expression of TLR-2 on the cell surface of innate and adaptive cells of patients’ [...] Read more.
The present study aimed to identify in patients with severe COVID-19 and acute respiratory distress syndrome (ARDS) the association between rs3804099 and rs3804100 (TLR2) and evaluate the expression of TLR-2 on the cell surface of innate and adaptive cells of patients’ carriers of C allele in at least one genetic variant. We genotyped 1018 patients with COVID-19 and ARDS. According to genotype, a subgroup of 12 patients was selected to stimulate peripheral blood mononuclear cells (PBMCs) with spike and LPS + spike. We evaluated soluble molecules in cell culture supernatants. The C allele in TLR2 (rs3804099, rs3804100) is not associated with a risk of severe COVID-19; however, the presence of the C allele (rs3804099 or rs3804100) affects the TLR-2 ability to respond to a spike of SARS-CoV-2 correctly. The reference group (genotype TT) downregulated the frequency of non-switched TLR-2+ B cells in response to spike stimulus; however, the allele’s C carriers group is unable to induce this regulation, but they produce high levels of IL-10, IL-6, and TNF-α by an independent pathway of TLR-2. Findings showed that TT genotypes (rs3804099 and rs3804100) affect the non-switched TLR-2+ B cell distribution. Genotype TT (rs3804099 and rs3804100) affects the TLR-2’s ability to respond to a spike of SARS-CoV-2. However, the C allele had increased IL-10, IL-6, and TNF-α by stimulation with spike and LPS. Full article
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Figure 1
<p>TLR-2 expression in monocytes of patients with COVID-19 according to genotypes (rs3804099 and rs3804100). They were stimulated for 24 h with spike protein (1 µg/mL) or spike + LPS (1 µg/mL). An unstimulated condition was included as a control stimulation (Unstimulated). (<b>A</b>) Representative dot plots show the limitation of CD2-D3−, then the gate CD14+HLA-DR+. (<b>B</b>) The frequency of TLR -2+ monocytes is reported, and each dot represents an independent patient. Data are expressed as median and IQR values. The statistical comparisons were performed using the Kruskal-Wallis test. Blue squares are used for the reference group, and red circles for the allele C carriers’ group.</p>
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<p>Spike decreased TLR-2 frequency in non-switched B-cells subset by patients with the TT genotypes (rs3804099 and rs3804100). Mononuclear cells from two groups of patients were stimulated for 24 h with spike protein (1 µg/mL) or spike + LPS (1 µg/mL). An unstimulated condition was included as a control stimulation (Unstimulated). (<b>A</b>) Representative dot plots show the B -cells subsets distribution based on CD27 and IgD expression as naive (IgD + CD27 −), non-switched (IgD + CD27+), or switched (IgD -CD27+). Analysis of TLR-4+ B -cells subsets frequencies for (<b>B</b>) naïve, (<b>C</b>) non-switched, and (<b>D</b>) switched. Data were represented as median and IQR values. The Kruskal -Wallis test performed statistical comparisons, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Blue squares are used for the reference group, and red circles for the allele C carriers’ group.</p>
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<p>Activated B-cell subsets do not change the frequency of TLR-2+ when stimulated. Mononuclear cells from two groups of patients were stimulated for 24 h with spike protein (1 µg/mL) or spike + LPS (1 µg/mL). An unstimulated condition was included as a control stimulation (unstimulated). (<b>A</b>) Representative dot plots show the distribution of activated B-cell subsets based on IgM and CD69 expression. The frequency of activated B-cell subsets positive to TLR-4 was analyzed; thus, TLR-4+ in (<b>B</b>) naïve, (<b>C</b>) non-switched, and (<b>D</b>) switched are shown. Data are represented as median and IQR values. The Kruskal–Wallis test performed statistical comparisons, * <span class="html-italic">p</span> &lt; 0.05. Blue squares are used for the reference group, and red circles for the allele C carriers’ group.</p>
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<p>The TLR-2 frequencies in activated CD8-T cells decreased in patients with the TT genotypes (rs3804099 and rs3804100). Mononuclear cells from two groups of patients were stimulated for 24 h with spike protein (1 µg/mL) or spike + LPS (1 µg/mL). An unstimulated condition was included as a control stimulation (unstimulated). (<b>A</b>) Representative dot plots show the CD8+ T-cell based on CD3 and CD8 expression. (<b>B</b>) Frequency of total CD8+ T-cell and TLR-4+CD8+ T cell. (<b>C</b>) Representative dot plots show the CD69 expression in the CD8+ T-cell gate. (<b>D</b>) Frequency of CD69+CD8+ T-cell, TLR-4+CD69+CD8+ T cell. Data were represented as median and IQR values. The Kruskal-Wallis test performed statistical comparisons, * <span class="html-italic">p</span> &lt; 0.05. Blue squares are used for the reference group, and red circles for the allele C carriers’ group.</p>
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<p>The C allele (rs3804099 or rs3804100) does not modify the secretion of inflammatory or cytotoxic cytokines by PBMCs. Mononuclear cells from two groups of patients were stimulated for 24 h with spike protein (1 µg/mL) or spike + LPS (1 µg/mL each one). An unstimulated condition was included as a control stimulation (unstimulated). The cytotoxic LEGENDplex<sup>TM</sup> panel assessed culture supernatants for nine protein markers. Data are represented as median and IQR values. The Kruskal–Wallis test performed statistical comparisons, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Blue squares are used for the reference group, and red circles for the allele C carriers’ group.</p>
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12 pages, 1735 KiB  
Article
Peptide TaY Attenuates Inflammatory Responses by Interacting with Myeloid Differentiation 2 and Inhibiting NF-κB Signaling Pathway
by Junyong Wang, Yichen Zhou, Jing Zhang, Yucui Tong, Zaheer Abbas, Xuelian Zhao, Zhenzhen Li, Haosen Zhang, Sichao Chen, Dayong Si, Rijun Zhang and Xubiao Wei
Molecules 2024, 29(20), 4843; https://doi.org/10.3390/molecules29204843 - 13 Oct 2024
Viewed by 442
Abstract
A balanced inflammatory response is crucial for the organism to defend against external infections, however, an exaggerated response may lead to detrimental effects, including tissue damage and even the onset of disease. Therefore, anti-inflammatory drugs are essential for the rational control of inflammation. [...] Read more.
A balanced inflammatory response is crucial for the organism to defend against external infections, however, an exaggerated response may lead to detrimental effects, including tissue damage and even the onset of disease. Therefore, anti-inflammatory drugs are essential for the rational control of inflammation. In this study, we found that a previously screened peptide TaY (KEKKEVVEYGPSSYGYG) was able to inhibit the LPS-induced RAW264.7 inflammatory response by decreasing a series of proinflammatory cytokines, such as TNF-α, IL-6, and nitric oxide (NO). To elucidate the underlying mechanism, we conducted further investigations. Western blot analysis showed that TaY reduced the phosphorylation of key proteins (IKK-α/β, IκB-α,NF-κB (P65)) in the TLR4-NF-κB signaling pathway and inhibited the inflammatory response. Furthermore, molecular docking and molecular dynamic simulations suggested that TaY binds to the hydrophobic pocket of MD2 through hydrogen bonding and hydrophobic interactions, potentially competing with LPS for MD2 binding. Collectively, TaY is a promising candidate for the development of novel therapeutic strategies against inflammatory disorders. Full article
(This article belongs to the Section Medicinal Chemistry)
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<p>TaY reduces the NO level of LPS-induced RAW264.7 inflammation. (<b>a</b>) Cytotoxicity assay of TaY on RAW264.7; (<b>b</b>) effect of different concentrations of TaY on NO production; (<b>c</b>) TaY reduces the mRNA expression level of iNOS; (<b>d</b>) Western blot results of TaY reducing the expression of iNOS proteins, β-actin as the reference protein; (<b>e</b>) grayscale analysis of Western blot results. The data are presented as the mean ± SD (n = 3). NS, <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>TaY reduces the proinflammatory cytokine of LPS-induced RAW 264.7 inflammation; (<b>a</b>,<b>b</b>) ELISA results of TNF-α and IL-6; (<b>c</b>,<b>d</b>) RT-PCR results of TNF-α and IL-6. The data are presented as the mean ± SD (n = 3). * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>TaY inhibits LPS activation of the TLR4/NF-κB signaling pathway. (<b>a</b>) Western blot results for IKK, IκBα, P65 proteins, and their phosphorylated forms, with β-Actin as the reference protein; (<b>b</b>–<b>d</b>) grayscale quantitative results for the proteins of P-IKK-α/β/IKK-α/β, P-IκBα/IκBα, and P-P65/P65, respectively. The data are presented as the mean ± SD (n = 3). * <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Molecular dynamics simulation results of TaY and MD2. (<b>a</b>) The root mean square deviation (RMSD) value for MD2 and TaY. (<b>b</b>) The radius of gyration (Rg) value for MD2 and TaY.</p>
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<p>TaY can bind to the hydrophobic pocket binding domains of MD2. The overall 3D conformation (<b>a</b>), local interaction 3D conformation (<b>b</b>), and 2D conformation (<b>c</b>) of TaY docked to MD2 (<b>d</b>). The hydrophobic interaction surface demonstrating the results of the docking of TaY to MD2.</p>
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18 pages, 4133 KiB  
Article
Comparative Transcriptome Analysis of Bovine, Porcine, and Sheep Muscle Using Interpretable Machine Learning Models
by Yaqiang Guo, Shuai Li, Rigela Na, Lili Guo, Chenxi Huo, Lin Zhu, Caixia Shi, Risu Na, Mingjuan Gu and Wenguang Zhang
Animals 2024, 14(20), 2947; https://doi.org/10.3390/ani14202947 - 12 Oct 2024
Viewed by 304
Abstract
The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat [...] Read more.
The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (n = 113), 94 bovine (n = 94), and 68 ovine (n = 68) specimens. We employed nine machine learning models, such as Support Vector Classifier (SVC) and Support Vector Machine (SVM). Applying the SHapley Additive exPlanations (SHAP) method, we analyzed the muscle transcriptome data of cattle, pigs, and sheep. The optimal model, adaptive boosting (AdaBoost), identified key genes potentially influencing muscle growth and development across the three species, termed SHAP genes. Among these, 41 genes (including NANOG, ADAMTS8, LHX3, and TLR9) were consistently expressed in all three species, designated as homologous genes. Specific candidate genes for cattle included SLC47A1, IGSF1, IRF4, EIF3F, CGAS, ZSWIM9, RROB1, and ABHD18; for pigs, DRP2 and COL12A1; and for sheep, only COL10A1. Through the analysis of SHAP genes utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, relevant pathways such as ether lipid metabolism, cortisol synthesis and secretion, and calcium signaling pathways have been identified, revealing their pivotal roles in muscle growth and development. Full article
(This article belongs to the Section Mammals)
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<p>Preprocessing of muscle transcript sample expression data prior to model construction. (<b>a</b>) Principal component analysis plot used for filtering out low-expression genes. (<b>b</b>) Principal component analysis plot after the removal of low-expression genes, batch correction, and normalization. (<b>c</b>) Quality control of expression data and average gene expression analysis per 10% gradient.</p>
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<p>Evaluation of the classification performance of nine machine learning models on the test set. (<b>a</b>) Support Vector Classifier (SVC), (<b>b</b>) Support Vector Machine (SVM), (<b>c</b>) deep neural network (DNN), (<b>d</b>) recurrent neural network (RNN), (<b>e</b>) logistic regression (LR), (<b>f</b>) decision tree (DT), (<b>g</b>) k-nearest neighbors (KNN), (<b>h</b>) Naive Bayes (NB), (<b>i</b>) AdaBoost. In the confusion matrix, the modules where the species names correspond both horizontally and vertically represent the model’s accurate predictions of the actual conditions. The modules on either side of the diagonal indicate erroneous predictions of species names. Each species in the test set has a transcription sample size of thirty.</p>
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<p>Analysis of SHAP genes in three species: <span class="html-italic">cattle</span>, pig, and <span class="html-italic">sheep</span>. (<b>a</b>) Venn diagram illustrating the distribution of SHAP genes among different species. (<b>b</b>) Expression levels of 41 homologous genes in the three species depicted on a bar graph, with gene names labeled on the horizontal axis and the corresponding expression levels on the vertical axis. (<b>c</b>) Correlation analysis among 41 genes in the bovine muscle transcriptome is presented, with gene names labeled on both the horizontal and vertical axes. (<b>d</b>) Correlation analysis among 41 genes in the porcine muscle transcriptome is shown. (<b>e</b>) Correlation analysis among 41 genes in the <span class="html-italic">sheep</span> muscle transcriptome is displayed.</p>
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<p>Intraspecies mean expression and interspecies correlation analysis of bovine, porcine, and <span class="html-italic">sheep</span>-specific SHAP genes. (<b>a</b>) Analysis of the average expression of bovine muscle-specific SHAP genes. (<b>b</b>) Analysis of the average expression of pig muscle-specific SHAP genes. (<b>c</b>) Analysis of the average expression of <span class="html-italic">sheep</span> muscle-specific SHAP genes. (<b>d</b>) Analysis of the average expression of SHAP genes specific to <span class="html-italic">pigs</span> and <span class="html-italic">sheep</span>. (<b>e</b>) Analysis of the correlation among SHAP genes across bovine, porcine, and <span class="html-italic">sheep</span> species.</p>
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<p>Presentation of the KEGG and GO analyses of the 41 homologous genes identified by the interpretable ML model SHAP across <span class="html-italic">cattle</span>, pig, and <span class="html-italic">sheep</span> species. Panel (<b>a</b>) shows the results of the KEGG enrichment analysis for the 41 homologous genes, while panel (<b>b</b>) displays the GO analysis findings for these genes.</p>
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<p>WGCNA was conducted among three species: <span class="html-italic">cattle</span>, <span class="html-italic">pigs</span>, and <span class="html-italic">sheep</span>. (<b>a</b>) Eight modules were retained after the removal of low-quality genes and samples. (<b>b</b>) The number of genes contained in each module and the percentage of the total number of genes. (<b>c</b>) A heatmap illustrating the correlation between the three species of <span class="html-italic">cattle</span>, <span class="html-italic">pigs</span>, and <span class="html-italic">sheep</span> with the eight characterized modules after the addition of species phenotypes.</p>
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<p>PPI network analysis of 49, 49, and 48 SHAP genes in muscle of <span class="html-italic">cattle</span>, pig, and <span class="html-italic">sheep</span>. (<b>a</b>) PPI network map of 15 key genes in 49 SHAP genes in <span class="html-italic">cattle</span>. (<b>b</b>) PPI network maps of 12 key genes in 49 SHAP genes in <span class="html-italic">pigs</span>. (<b>c</b>) PPI network map of 8 key genes in 48 SHAP genes of <span class="html-italic">sheep</span>.</p>
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23 pages, 10070 KiB  
Article
Evaluation of the Anti-Inflammatory/Immunomodulatory Effect of Teucrium montanum L. Extract in Collagen-Induced Arthritis in Rats
by Biljana Bufan, Mirjana Marčetić, Jasmina Djuretić, Ivana Ćuruvija, Veljko Blagojević, Dragana D. Božić, Violeta Milutinović, Radmila Janković, Jelena Sopta, Jelena Kotur-Stevuljević and Nevena Arsenović-Ranin
Biology 2024, 13(10), 818; https://doi.org/10.3390/biology13100818 - 12 Oct 2024
Viewed by 252
Abstract
The anti-inflammatory/immunomodulatory effects of Teucrium montanum L. (TM), a plant distributed in the Mediterranean region, have been insufficiently examined. The effects of the TM ethanol extract were tested in a rat collagen-induced arthritis (CIA) model of rheumatoid arthritis. LC-MS was used for the [...] Read more.
The anti-inflammatory/immunomodulatory effects of Teucrium montanum L. (TM), a plant distributed in the Mediterranean region, have been insufficiently examined. The effects of the TM ethanol extract were tested in a rat collagen-induced arthritis (CIA) model of rheumatoid arthritis. LC-MS was used for the phytochemical analysis of the TM extract. Dark Agouti rats were immunized with bovine type II collagen (CII) in incomplete Freund’s adjuvant for CIA, and treated with 100 or 200 mg/kg of TM extract daily via oral administration. Clinical and histopathological evaluations and a flow cytometric analysis of the phenotypic and functional characteristics of splenocytes and draining lymph node cells were performed. The cytokines in the paw tissue culture supernatants and anti-CII antibodies in serum were determined by ELISA. The TM extract, with the dominant components verbascoside and luteolin 7-O-rutinoside, reduced the arthritic score and ankle joint inflammation in CIA rats, promoted the antioxidant profile in serum, and lowered pro-inflammatory TNF-α, IL-6 and IL-1β production. It suppressed the activation status of CD11b+ cells by lowering CD86, MHCII and TLR-4 expression, and promoted the Th17/T regulatory cell (Tregs) balance towards Tregs. A lower frequency of B cells was accompanied by a lower level of anti-CII antibodies in treated rats. These findings imply the favorable effect of TM extract on the clinical presentation of CIA, suggesting its anti-inflammatory/immunomodulatory action and potential therapeutic effect. Full article
(This article belongs to the Special Issue Animal Models of Arthritis)
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<p>The effect of <span class="html-italic">T. montanum</span> ethanol extract treatment on the clinical course of CIA in rats. Daily arthritic score (median with interquartile range) in CIA rats and CIA rats treated with 100 mg/kg of <span class="html-italic">T. montanum</span> extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200) from the 13th day until the 22nd day post-immunization, with bovine collagen type II in incomplete Freund’s adjuvant. Clinical signs (joint swelling and redness) of arthritis were graded on an arbitrary scale as follows: 1 point for each inflamed metacarpophalangeal/metatarsophalangeal joint or proximal interphalangeal joint of each toe, and 5 points for an inflamed wrist/ankle joint. Thus, the maximal individual score for each paw was 15, and the highest arthritis score was 60. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001 for CIA vs. CIA + TM100; ## <span class="html-italic">p</span> ≤ 0.01, ### <span class="html-italic">p</span> ≤ 0.001 for CIA vs. CIA + TM200; <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>Histopathological analysis of ankle joints of CIA rats treated with <span class="html-italic">T. montanum</span> extract. Representative microphotographs from H&amp;E-stained sections of ankle joints of CIA rats, CIA rats treated with 100 mg/kg of <span class="html-italic">T. montanum</span> extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). (<b>a</b>,<b>e</b>) HC: no evidence of inflammation, no cartilage destruction, regular subchondral bone; (<b>b</b>,<b>f</b>) CIA: intensive diffuse mononuclear infiltrates, hyperplasia of the synovial layer with superficial necrosis (black arrow), and cartilage and bone destruction (green arrow); (<b>c</b>,<b>g</b>) CIA + TM100: reduction in inflammation, focal mononuclear infiltrate, no necrosis, papillary synovial appearance (blue arrow), and only cartilage destruction; (<b>d</b>,<b>h</b>) CIA + TM200: no inflammation, no necrosis, focally synovial fatty metaplasia and fibrosis (red arrow), and minimal cartilage destruction. Magnification: 100× and 400×.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on the oxidative stress parameter levels in the serum of CIA rats. The bar graphs represent the levels of antioxidative parameters: total antioxidant capacity (TAC), superoxide dismutase (SOD), sulfhydryl groups (SHG); prooxidative parameters: total oxidant capacity (TOC), advanced oxidation protein products (AOPPs), pro-oxidant–antioxidant balance (PAB), and TOC/TAC ratio. These parameters were determined in the sera of CIA rats, CIA rats treated with 100 mg/kg of <span class="html-italic">T. montanum</span> extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001 for TM-treated CIA vs. CIA; ### <span class="html-italic">p</span> ≤ 0.001 for CIA + TM100 vs. CIA + TM200; + <span class="html-italic">p</span> ≤ 0.05, ++ <span class="html-italic">p</span> ≤ 0.01 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on pro- and anti-inflammatory cytokine production in the arthritic paws, draining lymph nodes and spleens of CIA rats. (<b>A</b>) The bar graphs represent the concentrations of TNF-α, IL-6 and IL-10 in the supernatants from the hind paw tissue cultures (normalized to the paw weight) of CIA rats, CIA rats treated with 100 mg/kg of T. montanum extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. (<b>B</b>,<b>C</b>) The bar graphs represent the percentage of TNF-α+ and IL-1β+ cells within the CD11b+ cells of (<b>B</b>) draining lymph node (dLN) cells and the (<b>C</b>) splenocytes of CIA rats, CIA + TM100, CIA + TM200 and HC. Results are expressed as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of TNF-α+ cells and IL-1β+ cells among CD11b+ cells from the (<b>B</b>) dLNs and (<b>C</b>) spleens gated, as shown in <a href="#app1-biology-13-00818" class="html-app">Figure S1</a>. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001 for TM-treated CIA vs. CIA; ## <span class="html-italic">p</span> ≤ 0.01 for CIA + TM100 vs. CIA + TM200; + <span class="html-italic">p</span> ≤ 0.05, ++ <span class="html-italic">p</span> ≤ 0.01, and +++ <span class="html-italic">p</span> ≤ 0.001 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on the expression of MHC II molecules in cells from the draining lymph nodes and spleens of CIA rats. The bar graphs represent the percentages of MHC II+ cells and the mean fluorescent intensity (MFI) of MHC II+ cells within (upper row) the draining lymph node (dLN) cells and (lower row) splenocytes of CIA rats, CIA rats treated with 100 mg/kg of T. montanum extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of MHC II+ cells and the MFI (in parenthesis) of MHC II+ cells of the dLN cells and splenocytes gated, as shown in the FSC/SSC dot plots in <a href="#app1-biology-13-00818" class="html-app">Figure S1A</a>. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001 for TM-treated CIA vs. CIA; ## <span class="html-italic">p</span> ≤ 0.01 for CIA + TM100 vs. CIA + TM200; + <span class="html-italic">p</span> ≤ 0.05, ++ <span class="html-italic">p</span> ≤ 0.01, and +++ <span class="html-italic">p</span> ≤ 0.001 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on CD86 and TLR4 expression in cells from the draining lymph nodes and spleens of CIA rats. (<b>A</b>) The bar graphs represent the percentage of CD11b+ cells within the draining lymph node (dLN) cells and splenocytes of CIA rats, CIA rats treated with 100 mg/kg of T. montanum extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. The gating strategy for the flow cytometry analysis of the CD11b staining of dLN cells and splenocytes is presented in <a href="#app1-biology-13-00818" class="html-app">Figure S1A</a>. The bar graphs represent the percentage of (<b>B</b>) CD86+ and (<b>C</b>) TLR4+cells within the dLN cells and splenocytes of CIA rats, CIA + TM100, CIA + TM200, and HC. Results are expressed as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of (<b>B</b>) CD86+ cells and (<b>C</b>) TLR4+ cells among CD11b+ cells from the dLN cells or splenocytes gated, as shown in <a href="#app1-biology-13-00818" class="html-app">Figure S1A</a>. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001 for TM-treated CIA vs. CIA; # <span class="html-italic">p</span> ≤ 0.05 for CIA + TM100 vs. CIA + TM200; + <span class="html-italic">p</span> ≤ 0.05, ++ <span class="html-italic">p</span> ≤ 0.01, and +++ <span class="html-italic">p</span> ≤ 0.001 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on the frequency of TCRαβ+ cells and CD4+ and CD8+ subpopulations in the draining lymph nodes and spleens of CIA rats. The bar graphs represent the percentages of TCRαβ+ cells and CD4+ and CD8+ cells among the TCRαβ+ cell population and CD4+/CD8+ cell ratio in the lymphocytes of the (<b>A</b>) draining lymph nodes (dLN) and (<b>B</b>) spleens of CIA rats, CIA rats treated with 100 mg/kg of T. montanum extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of CD4+ and CD8+ cells among TCRαβ+ cells from the lymphocyte gate among (<b>A</b>) dLN cells and (<b>B</b>) splenocytes. The gating strategy for the flow cytometry analysis of TCRαβ+ cells from the lymphocyte gate is presented in <a href="#app1-biology-13-00818" class="html-app">Figure S1B</a>. ** <span class="html-italic">p</span> ≤ 0.01 for TM-treated CIA vs. CIA; # <span class="html-italic">p</span> ≤ 0.05, and ## <span class="html-italic">p</span> ≤ 0.01 for CIA + TM100 vs. CIA + TM200; + <span class="html-italic">p</span> ≤ 0.05, and ++ <span class="html-italic">p</span> ≤ 0.01 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>The effect of <span class="html-italic">T. montanum</span> extract on frequency of Th17, Th1, and T regulatory cells in the draining lymph nodes and spleens of CIA rats. The bar graphs represent the percentage of IL17+, IFN-γ+ and FoxP3+ cells among CD4+ cells, i.e., Th17, Th1 and T regulatory (Treg) cells, and the Th17/Treg cell ratio in the lymphocytes of the draining lymph nodes (dLN) and spleens of CIA rats, CIA rats treated with 100 mg/kg of T. montanum extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are expressed as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of IL-17+ cells, IFN-γ+ cells and FoxP3+ cells among CD4+ cells from the lymphocyte gate among dLN cells and splenocytes, whose gating strategy is shown in <a href="#app1-biology-13-00818" class="html-app">Figure S1C</a>. * <span class="html-italic">p</span> ≤ 0.05, and ** <span class="html-italic">p</span> ≤ 0.01 for TM-treated CIA vs. CIA; + <span class="html-italic">p</span> ≤ 0.05 and ++ <span class="html-italic">p</span> ≤ 0.01 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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<p>Effect of <span class="html-italic">T. montanum</span> extract on the frequency of B cells and the circulating level of anti-CII antibodies. (<b>A</b>) The bar graphs represent the percentages of CD45RA+ cells in the lymphocytes of the draining lymph nodes (dLN) and spleens of CIA rats, CIA rats treated with 100 mg/kg of <span class="html-italic">T. montanum</span> extract (TM) (CIA + TM100) and 200 mg/kg of TM (CIA + TM200), and healthy control rats (HC). Results are presented as median with interquartile range. Representative flow cytometry dot plots indicate the percentage of CD45RA+ cells in the lymphocyte gate among dLN cells and splenocytes. The lymphocytes were gated as shown in <a href="#app1-biology-13-00818" class="html-app">Figure S1B</a>. (<b>B</b>) The bar graphs represent the levels [optical density (OD) at 492/620 nm] of anti-CII antibodies in the serum (1:100 dilution) of CIA, CIA + TM100, CIA + TM200, and HC rats. Results are presented as median with interquartile range. * <span class="html-italic">p</span> ≤ 0.05, and ** <span class="html-italic">p</span> ≤ 0.01. for TM-treated CIA vs. CIA; ++ <span class="html-italic">p</span> ≤ 0.01, and +++ <span class="html-italic">p</span> ≤ 0.001 vs. HC. <span class="html-italic">n</span> = 6 rats/group.</p>
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19 pages, 1819 KiB  
Review
Interleukin-1 Receptor-Associated Kinase 1 in Cancer Metastasis and Therapeutic Resistance: Mechanistic Insights and Translational Advances
by Mariana K. Najjar, Munazza S. Khan, Chuling Zhuang, Ankush Chandra and Hui-Wen Lo
Cells 2024, 13(20), 1690; https://doi.org/10.3390/cells13201690 - 12 Oct 2024
Viewed by 685
Abstract
Interleukin-1 Receptor Associated Kinase 1 (IRAK1) is a serine/threonine kinase that plays a critical role as a signaling transducer of the activated Toll-like receptor (TLR)/Interleukin-1 receptor (IL-1R) signaling pathway in both immune cells and cancer cells. Upon hyperphosphorylation by IRAK4, IRAK1 forms a [...] Read more.
Interleukin-1 Receptor Associated Kinase 1 (IRAK1) is a serine/threonine kinase that plays a critical role as a signaling transducer of the activated Toll-like receptor (TLR)/Interleukin-1 receptor (IL-1R) signaling pathway in both immune cells and cancer cells. Upon hyperphosphorylation by IRAK4, IRAK1 forms a complex with TRAF6, which results in the eventual activation of the NF-κB and MAPK pathways. IRAK1 can translocate to the nucleus where it phosphorylates STAT3 transcription factor, leading to enhanced IL-10 gene expression. In immune cells, activated IRAK1 coordinates innate immunity against pathogens and mediates inflammatory responses. In cancer cells, IRAK1 is frequently activated, and the activation is linked to the progression and therapeutic resistance of various types of cancers. Consequently, IRAK1 is considered a promising cancer drug target and IRAK1 inhibitors have been developed and evaluated preclinically and clinically. This is a comprehensive review that summarizes the roles of IRAK1 in regulating metastasis-related signaling pathways of importance to cancer cell proliferation, cancer stem cells, and dissemination. This review also covers the significance of IRAK1 in mediating cancer resistance to therapy and the underlying molecular mechanisms, including the evasion of apoptosis and maintenance of an inflammatory tumor microenvironment. Finally, we provide timely updates on the development of IRAK1-targeted therapy for human cancers. Full article
(This article belongs to the Special Issue Cellular and Molecular Mechanisms of Cancer Metastasis—2nd Edition)
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<p>An overview of the TLR/IL-1R signaling pathway. The TLR/IL-1R pathway is activated following TLR or IL-1R binding to their respective ligands, including PAMPs, DAMPs, LPSs, or cytokines from the IL-1 family. This binding triggers the recruitment of MyD88 and assembly of the myddosome complex, comprising MyD88, IRAK4, IRAK2, and IRAK1. IRAK4 phosphorylates IRAK1, initiating an auto-phosphorylation cascade that results in hyperphosphorylated IRAK1. Hyperphosphorylated IRAK1 then dissociates from the myddosome complex and associates with TRAF6. IRAK-M functions to inhibit the dissociation of IRAK1 from the myddosome. The interaction between IRAK1 and TRAF6 activates the IKK complex, leading to the degradation of IκB, which releases NF-κB for nuclear translocation and transcriptional activity. Additionally, IRAK1 and TRAF6 interaction leads to the assembly of the catalytically active TAK1-TAB complex, which activates the MAPK pathway. In addition to activating the IKK complex, MAPK activates downstream effectors ERK, p38, and JNK, all of which are involved in the transcriptional activation of several inflammatory cytokines through AP-1. Moreover, IRAK1 translocates to the nucleus, where it phosphorylates STAT3 at the serine 727 residue, promoting the IL-10 gene activation. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Roles of IRAK1 in the metastatic cascade. (1) IRAK1 regulates the epithelial–mesenchymal transition. It upregulates the expression of mesenchymal markers (N-cadherin, vimentin, and snail), along with endopeptidases (MMP-2 and MMP-9), for degradation and remodeling of the extracellular matrix (ECM). (2) IRAK1 promotes angiogenesis. It elevates the expression of pro-angiogenic factors (VEGF, CXCL1, and IL-8), and is involved in vascular smooth muscle cell (VSMC) proliferation. (3) IRAK1 facilitates metastatic colonization. It promotes survival through releasing pro-growth cytokines and activating mitotic cell cycle-related factors (CDK1 and Cdc45) and cell division pathway factors (Cdc7 and MCM2). It facilitates the extravasation of circulating tumor cells (CTCs) through upregulating the expression of adhesion molecules (VCAM1 and ICAM1). It supports cell colonization at secondary sites by modulating the TME through the release of pro-growth and pro-survival cytokines, and the promotion of immune evasion. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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13 pages, 8204 KiB  
Article
Catecholamines Attenuate LPS-Induced Inflammation through β2 Adrenergic Receptor Activation- and PKA Phosphorylation-Mediated TLR4 Downregulation in Macrophages
by Cong Wang, Guo-Gang Feng, Junko Takagi, Yoshihiro Fujiwara, Tsuyoshi Sano and Hideaki Note
Curr. Issues Mol. Biol. 2024, 46(10), 11336-11348; https://doi.org/10.3390/cimb46100675 (registering DOI) - 12 Oct 2024
Viewed by 213
Abstract
Inflammation is a tightly regulated process involving immune receptor recognition, immune cell migration, inflammatory mediator secretion, and pathogen elimination, all essential for combating infection and restoring damaged tissue. However, excessive inflammatory responses drive various human diseases. The autonomic nervous system (ANS) is known [...] Read more.
Inflammation is a tightly regulated process involving immune receptor recognition, immune cell migration, inflammatory mediator secretion, and pathogen elimination, all essential for combating infection and restoring damaged tissue. However, excessive inflammatory responses drive various human diseases. The autonomic nervous system (ANS) is known to regulate inflammatory responses; however, the detailed mechanisms underlying this regulation remain incompletely understood. Herein, we aimed to study the anti-inflammatory effects and mechanism of action of the ANS in RAW264.7 cells. Quantitative PCR and immunoblotting assays were used to assess lipopolysaccharide (LPS)-induced tumor necrosis factor α (TNFα) expression. The anti-inflammatory effects of catecholamines (adrenaline, noradrenaline, and dopamine) and acetylcholine were examined in LPS-treated cells to identify the receptors involved. Catecholamines inhibited LPS-induced TNFα expression by activating the β2 adrenergic receptor (β2-AR). β2-AR activation in turn downregulated the expression of Toll-like receptor 4 (TLR4) by stimulating protein kinase A (PKA) phosphorylation, resulting in the suppression of TNFα levels. Collectively, our findings reveal a novel mechanism underlying the inhibitory effect of catecholamines on LPS-induced inflammatory responses, whereby β2-AR activation and PKA phosphorylation downregulate TLR4 expression in macrophages. These findings could provide valuable insights for the treatment of inflammatory diseases and anti-inflammatory drug development. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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<p>Time-dependent effect of LPS on TNFα production in RAW264.7 cells. TNFα expression was measured at the mRNA level (upper panel) via RT-qPCR and at the protein level (lower panel) through immunoblotting, following treatment with 1 μg/mL LPS at various time points (0–12 h). Data from qPCR analyses are expressed as mean ± SE (<span class="html-italic">n</span> = 3). * <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 vs. without LPS treatment (0 time). For the immunoblotting, one representative result from three independent experiments is shown. GAPDH was used as a loading control.</p>
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<p>Concentration-dependent effects of adrenaline (Ad), noradrenaline (Nad), dopamine (DA), and acetylcholine (ACh) on LPS-induced TNFα production in RAW264.7 cells. Cells were treated with varying concentrations of (<b>a</b>) Ad (0.01–10 μM), (<b>b</b>) Nad (0.01–10 μM), (<b>c</b>) DA (0.1–100 μM), and (<b>d</b>) ACh (0.01–10 μM), in the presence or absence of 1 μg/mL LPS for 2 h. TNFα expression was assessed at both the mRNA level (upper panel) via RT-qPCR and the protein level (lower panel) via immunoblotting. Data from qPCR analyses are expressed as mean ± SE (<span class="html-italic">n</span> = 3). *** <span class="html-italic">p</span> &lt; 0.001 vs. without any treatment; ## <span class="html-italic">p</span> &lt; 0.01 and ### <span class="html-italic">p</span> &lt; 0.001 vs. with LPS treatment. For the immunoblotting, one representative result from three independent experiments is shown. GAPDH was used as a loading control.</p>
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<p>Roles of the β2 adrenergic receptor (β2-AR) in the effects of Ad, Nad, and DA on TNFα expression in LPS-treated RAW264.7 cells. (<b>a</b>) AR expression in RAW 264.7 cells. Effects of phentolamine, propranolol, metoprolol, or ICI 118,551 on TNFα expression in cells treated with LPS with or without (<b>b</b>) Ad, (<b>c</b>) Nad, and (<b>d</b>) DA. (<b>e</b>) Cells were also treated with LPS with or without metaramine, isoproterenol, dobutamine, or fenoterol. Data from qPCR analyses are expressed as means ± SE (<span class="html-italic">n</span> = 3). ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. without any treatment; # <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 vs. with LPS treatment. For the immunoblotting, one representative result from three independent experiments is shown. GAPDH was used as a loading control.</p>
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<p>Effects of β2-AR knockdown or overexpression on the expression of TNFα in LPS-treated RAW264.7 cells treated with Ad. Cells were transfected with the specific siRNA targeting β2-AR (β2-AR-siRNA) and control siRNA (Con-siRNA), and plasmid overexpressing β2-AR (pcDNA-β2-AR) and its control (pcDNA), respectively. (<b>a</b>,<b>c</b>) β2-AR expression was then examined via immunoblotting. (<b>b</b>,<b>d</b>) Transfected cells were treated with LPS with or without Ad for 2 h, and TNFα protein levels were determined (upper panel). One representative result from three independent experiments is shown. GAPDH was used as a loading control. The band density was quantitatively analyzed using ImageJ software. The results (lower panel in (<b>b</b>,<b>d</b>)) are expressed as mean ± SE (<span class="html-italic">n</span> = 3). *** <span class="html-italic">p</span> &lt; 0.001; # <span class="html-italic">p</span> &lt; 0.05 and ### <span class="html-italic">p</span> &lt; 0.001; ††† <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Roles of β2-AR activation in PKA phosphorylation (p-PKA) and TLR4 expression in RAW264.7 cells. (<b>a</b>,<b>b</b>) Cells were transfected as described above, and TLR4 protein levels were assessed. After transfection with pcDNA or pcDNA-β2-AR for 8 h, H89 was added and incubated for 10 h. Ad was then added and incubated for 2 h. PKA, p-PKA, and TLR4 protein levels were detected (upper panel in (<b>c</b>,<b>d</b>)). One representative result from three independent experiments is shown. GAPDH was used as a loading control. Band density was quantitatively analyzed using ImageJ software, and the results (lower panel) are expressed as means ± SE (<span class="html-italic">n</span> = 3). * <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">p</span> &lt; 0.05; † <span class="html-italic">p</span> &lt; 0.05.</p>
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16 pages, 6498 KiB  
Article
CCL21 Induces Plasmacytoid Dendritic Cell Migration and Activation in a Mouse Model of Glioblastoma
by Lei Zhao, Jack Shireman, Samantha Probelsky, Bailey Rigg, Xiaohu Wang, Wei X. Huff, Jae H. Kwon and Mahua Dey
Cancers 2024, 16(20), 3459; https://doi.org/10.3390/cancers16203459 - 12 Oct 2024
Viewed by 668
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells that are traditionally divided into two distinct subsets: myeloid DCs (mDCs) and plasmacytoid DCs (pDCs). pDCs are known for their ability to secrete large amounts of cytokine type I interferons (IFN- α). In our previous work, [...] Read more.
Dendritic cells (DCs) are professional antigen-presenting cells that are traditionally divided into two distinct subsets: myeloid DCs (mDCs) and plasmacytoid DCs (pDCs). pDCs are known for their ability to secrete large amounts of cytokine type I interferons (IFN- α). In our previous work, we have demonstrated that pDC infiltration promotes glioblastoma (GBM) tumor immunosuppression through decreased IFN-α secretion via TLR-9 signaling and increased suppressive function of regulatory T cells (Tregs) via increased IL-10 secretion, resulting in poor overall outcomes in mouse models of GBM. Further dissecting the overall mechanism of pDC-mediated GBM immunosuppression, in this study, we identified CCL21 as highly upregulated by multiple GBM cell lines, which recruit pDCs to tumor sites via CCL21-CCR7 signaling. Furthermore, pDCs are activated by CCL21 in the GBM microenvironment through intracellular signaling of β-arrestin and CIITA. Finally, we found that CCL21-treated pDCs directly suppress CD8+ T cell proliferation without affecting regulatory T cells (Tregs) differentiation, which is considered the canonical pathway of immunotolerant regulation. Taken together, our results show that pDCs play a multifaced role in GBM immunosuppression, and CCL21 could be a novel therapeutic target in GBM to overcome pDC-mediated immunosuppression. Full article
(This article belongs to the Special Issue Molecular Pathology of Brain Tumors)
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<p>CCL21 is highly expressed by mouse GBM cells. (<b>A</b>) Heatmap of altered protein from GL261 tumor-bearing brain and normal brain by mouse cytokine/chemokine array. The density of the CCL21 protein level is analyzed by ImageJ (n = 3). Data represent mean ± SEM. *** <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) Immunohistochemical (IHC) staining for CCL21 in GL261 tumor-bearing brain and normal brain. Scale bar = 50 μm. (<b>C</b>) Immunocytochemistry staining for intracellular CCL21 in GL261 and CT2A. Scale bar = 20 μm.</p>
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<p>GBM mediates pDC, not mDC, migration via CCL21. (<b>A</b>) Schematic of transwell migration assays followed by flow cytometry. (<b>B</b>) Quantification of transwell migration assay of pDCs and mDCs in response to CCL21 protein, tumor cells, and neutralized antibodies. (<b>C</b>) Quantification of transwell migration assay of ACKR4+ or ACKR4+/CCR7+ sorted pDCs in response to CCL21 protein. (<b>D</b>) Schematic of IncuCyte migration assay. (<b>E</b>) Dot graph and radar plot depicting total distance traveled by pDCs in response to CCL21 protein and neutralized antibodies. (<b>F</b>) Dot graph and radar plot depicting total distance traveled by mDCs in response to CCL21 protein and neutralized antibodies. N = 60 cells, data represent mean ± SEM. * <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>GBM mediates pDC activation via CCL21. (<b>A</b>) Schematic representation of tissue dissociation, qPCR, and flow cytometry experiments. (<b>B</b>) Distribution of pDCs, MHC-II+ pDCs, and MHC-II+/CIITA+ pDCs in different immune organs from normal and tumor-bearing mice. (<b>C</b>) Increased MHC-II+/ACKR4+ pDCs were found in tumor-bearing brains, compared with normal brains or mDCs in tumor-bearing brains. (<b>D</b>) ACKR4 gene expression in pDCs and mDCs sorted from the spleen, LN, and brain of GL261-bearing mice (n = 4). (<b>E</b>) Single-nucleus RNA sequencing data in cervical cancer. The figure is exported from Single Cell Portal, BROAD [<a href="#B36-cancers-16-03459" class="html-bibr">36</a>,<a href="#B37-cancers-16-03459" class="html-bibr">37</a>]. (<b>F</b>) Activation of pDCs, not mDCs, in response to CCL21 protein, tumor cell co-culture, and neutralized antibodies. n ≥ 3, data represent mean ± SEM. NS no significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>CCL21 induces pDC activation through the CCR7/ACKR4—β-arrestin/CIITA pathway. (<b>A</b>) qPCR was used to quantify gene expression and is reported as fold change normalized to untreated controls. (<b>B</b>) Representative immunostaining images of β-arrestin and CIITA and (<b>C</b>) quantification of the immunostaining images. Nuclear translocation was quantified by counting positive cell numbers. Scale bar = 50 μm. (<b>D</b>) Immunoblot analysis of β-arrestin and CIITA proteins in nuclear and cytoplasm fractions of pDCs and (<b>E</b>) immunoblot analysis of β-arrestin and CIITA proteins in nuclear and cytoplasm fractions of mDCs. Quantification: normalization to actin/LaminB1. Data represent mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>CCL21-induced pDCs show a regulatory phenotype. (<b>A</b>) Phenotypic analysis of pDCs with CCL21 treatment. (<b>B</b>) Flow cytometry analysis of TLR9 expression and cytokine secretion of CCL21-pretreated pDCs under CpG stimulation. (<b>C</b>) CD8+ T cells were co-cultured with pretreated pDCs. T cell population and proliferation were analyzed and quantified by flow cytometry. (<b>D</b>) CD4+ naïve T cells were co-cultured with pretreated pDCs. Foxp3+ T cell population and proliferation were analyzed and quantified by flow cytometry. (<b>E</b>) Mechanistic overview of CCL21 induction of pDCs. Data represent mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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19 pages, 2687 KiB  
Article
Influence of Donor-Specific Characteristics on Cytokine Responses in H3N2 Influenza A Virus Infection: New Insights from an Ex Vivo Model
by Chung-Guei Huang, Ming-Ju Hsieh, Yi-Cheng Wu, Po-Wei Huang, Ya-Jhu Lin, Kuo-Chien Tsao, Shin-Ru Shih and Li-Ang Lee
Int. J. Mol. Sci. 2024, 25(20), 10941; https://doi.org/10.3390/ijms252010941 - 11 Oct 2024
Viewed by 490
Abstract
Influenza A virus (IAV) is known for causing seasonal epidemics ranging from flu to more severe outcomes like pneumonia, cytokine storms, and acute respiratory distress syndrome. The innate immune response and inflammasome activation play pivotal roles in sensing, preventing, and clearing the infection, [...] Read more.
Influenza A virus (IAV) is known for causing seasonal epidemics ranging from flu to more severe outcomes like pneumonia, cytokine storms, and acute respiratory distress syndrome. The innate immune response and inflammasome activation play pivotal roles in sensing, preventing, and clearing the infection, as well as in the potential exacerbation of disease progression. This study examines the complex relationships between donor-specific characteristics and cytokine responses during H3N2 IAV infection using an ex vivo model. At 24 h post infection in 31 human lung explant tissue samples, key cytokines such as interleukin (IL)-6, IL-10, tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ) were upregulated. Interestingly, a history of lung cancer did not impact the acute immune response. However, cigarette smoking and programmed death-ligand 1 (PD-L1) expression on macrophages significantly increased IL-2 levels. Conversely, age inversely affected IL-4 levels, and diabetes mellitus negatively influenced IL-6 levels. Additionally, both diabetes mellitus and programmed cell death protein 1 (PD-1) expression on CD3+/CD4+ T cells negatively impacted TNF-α levels, while body mass index was inversely associated with IFN-γ production. Toll-like receptor 2 (TLR2) expression emerged as crucial in mediating acute innate and adaptive immune responses. These findings highlight the intricate interplay between individual physiological traits and immune responses during influenza infection, underscoring the importance of tailored and personalized approaches in IAV treatment and prevention. Full article
(This article belongs to the Special Issue Roles of Inflammasomes in Inflammatory Responses and Human Diseases)
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<p>Correlations between cytokine levels and molecular expressions post-H3N2 infection. (<b>a</b>) This panel illustrates the significant correlations between percentage differences in NP expression in epithelial cells, molecular markers, and secreted cytokine levels 24 h after ex vivo infection with H3N2 influenza A virus. (<b>b</b>) This panel highlights the significant correlations between percentage differences in NP expression in macrophages, molecular markers, and secreted cytokine levels following H3N2 infection. The depicted correlations are determined using Pearson tests for normally distributed variables and the Spearman correlation for non-normally distributed variables. Positive correlations are indicated by blue lines, while negative associations are shown in red. Only correlations with two-sided <span class="html-italic">p</span>-values less than 0.05 are illustrated, highlighting statistically significant relationships that may impact the immune response to H3N2 infection. Abbreviations: IL: interleukin; NP: nucleoprotein; PD-1: programmed cell death protein 1; PD-L1: programmed cell death ligand 1; TLR: Toll-like receptor.</p>
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<p>Flow cytometry analysis of molecular expressions in various cell populations during H3N2 infection. (<b>a</b>) Expressions of influenza A virus NP (green) in EpCAM<sup>+</sup> epithelial cells; (<b>b</b>) expressions of influenza A virus NP (green) in HLA-DR<sup>+</sup> macrophages; (<b>c</b>) expressions of PD-1 (orange) on CD3<sup>+</sup>/CD4<sup>+</sup> T cells; (<b>d</b>) expressions of PD-1 (blue) on CD3<sup>+</sup>/CD8<sup>+</sup> T cells. Abbreviations: EpCAM, epithelial cell adhesion molecule; HLA, human leukocyte antigen; NP, nucleoprotein; PD-1, programmed death 1.</p>
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<p>Flow cytometry analysis of TLR and PD-L1 expressions in cell populations during H3N2 infection. (<b>a</b>) Expressions of TLR1 (green) and/or TLR3 (green) on epithelial cells; (<b>b</b>) expressions of TLR1 (orange) and/or TLR3 (orange) on macrophages; (<b>c</b>) expressions of TLR2 (green) and/or PD-L1 (green) on epithelial cells; (<b>d</b>) expressions of TLR2 (orange) and/or PD-L1 (orange) on macrophages. Abbreviations: DN, double negative; PD-L1, programmed death-ligand 1; TLR, Toll-like receptor.</p>
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<p>Flow cytometry analysis of TLR and PD-L1 expressions in cell populations during H3N2 infection. (<b>a</b>) Expressions of TLR1 (green) and/or TLR3 (green) on epithelial cells; (<b>b</b>) expressions of TLR1 (orange) and/or TLR3 (orange) on macrophages; (<b>c</b>) expressions of TLR2 (green) and/or PD-L1 (green) on epithelial cells; (<b>d</b>) expressions of TLR2 (orange) and/or PD-L1 (orange) on macrophages. Abbreviations: DN, double negative; PD-L1, programmed death-ligand 1; TLR, Toll-like receptor.</p>
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18 pages, 1325 KiB  
Review
Macrophages as Potential Therapeutic Targets in Acute Myeloid Leukemia
by Oana Mesaros, Madalina Onciul, Emilia Matei, Corina Joldes, Laura Jimbu, Alexandra Neaga, Oana Serban, Mihnea Zdrenghea and Ana Maria Nanut
Biomedicines 2024, 12(10), 2306; https://doi.org/10.3390/biomedicines12102306 - 11 Oct 2024
Viewed by 802
Abstract
Acute myeloid leukemia (AML) is a heterogenous malignant hemopathy, and although new drugs have emerged recently, current treatment options still show limited efficacy. Therapy resistance remains a major concern due to its contribution to treatment failure, disease relapse, and increased mortality among patients. [...] Read more.
Acute myeloid leukemia (AML) is a heterogenous malignant hemopathy, and although new drugs have emerged recently, current treatment options still show limited efficacy. Therapy resistance remains a major concern due to its contribution to treatment failure, disease relapse, and increased mortality among patients. The underlying mechanisms of resistance to therapy are not fully understood, and it is crucial to address this challenge to improve therapy. Macrophages are immune cells found within the bone marrow microenvironment (BMME), of critical importance for leukemia development and progression. One defining feature of macrophages is their plasticity, which allows them to adapt to the variations in the microenvironment. While this adaptability is advantageous during wound healing, it can also be exploited in cancer scenarios. Thus, clinical and preclinical investigations that target macrophages as a therapeutic strategy appear promising. Existing research indicates that targeting macrophages could enhance the effectiveness of current AML treatments. This review addresses the importance of macrophages as therapeutic targets including relevant drugs investigated in clinical trials such as pexidartinib, magrolimab or bexmarilimab, but also provides new insights into lesser-known therapies, like macrophage receptor with a collagenous structure (MACRO) inhibitors and Toll-like receptor (TLR) agonists. Full article
(This article belongs to the Collection Advances in Leukocyte Biology)
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<p>TAMs and their functions: M1-like macrophages have antitumor effects and are activated by inflammatory cytokines. These macrophages produce chemokines like CXCL10, which play a crucial role in attracting and activating T cells. Additionally, M1-like macrophages actively phagocytose cancer cells while releasing TNF-α, ROS, and NO to target and eliminate neoplastic cells. In contrast, M2-like macrophages serve pro-tumor roles; they secrete factors that enhance and promote tumor growth. Furthermore, they produce immune-suppressive substances, which support the function of regulatory T cells. Abbreviations: TAMs = Tumour-associated macrophages; APCs = Antigen-presenting cells; TNF = Tumor necrosis factor; IL = Interleukin; CXCL = Chemokine (C-X-C motif) ligand; INF = Interferon; NO = Nitrogen oxide; ROS = Reactive oxygen species; PD = Programmed death; PDL = Programmed death ligand; TGF = Transforming growth factors; MMPs = Matrix metalloproteinases; EGF = Epidermal growth factor; FGF=Fibroblast growth factor; PDGF = Platelet-derived growth factor; VEGF = Vascular endothelial growth factor.</p>
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<p>CD47-SIRPα blockade: AML cells evade immune responses by CD47-SIRPα synergy, which provides a “do not eat me” signal. Blocking CD47-SIRPα interaction with magrolimab will induce direct tumor cell apoptosis, complement-mediated apoptosis, and immune cell phagocytosis [<a href="#B79-biomedicines-12-02306" class="html-bibr">79</a>].</p>
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19 pages, 8699 KiB  
Article
Exercise Promotes Hippocampal Neurogenesis in T2DM Mice via Irisin/TLR4/MyD88/NF-κB-Mediated Neuroinflammation Pathway
by Haocheng Xu, Xin Tian, Yuanxin Wang, Junjie Lin, Baishu Zhu, Chen Zhao, Bin Wang, Xin Zhang, Yu Sun, Nan Li, Xun Sun, Fanxi Zeng, Mingzhi Li, Xiquan Ya and Renqing Zhao
Biology 2024, 13(10), 809; https://doi.org/10.3390/biology13100809 - 10 Oct 2024
Viewed by 494
Abstract
Neuroinflammation is a major feature of type 2 diabetic mellitus (T2DM), adversely affecting hippocampal neurogenesis. However, the precise mechanism is not fully understood, and therapeutic approaches are currently lacking. Therefore, we determined the effects of exercise on neuroinflammation and hippocampal neurogenesis in T2DM [...] Read more.
Neuroinflammation is a major feature of type 2 diabetic mellitus (T2DM), adversely affecting hippocampal neurogenesis. However, the precise mechanism is not fully understood, and therapeutic approaches are currently lacking. Therefore, we determined the effects of exercise on neuroinflammation and hippocampal neurogenesis in T2DM mice, with a specific focus on understanding the role of the irisin and related cascade pathways in modulating the beneficial effects of exercise in these processes. Ten-week exercise significantly decreased T2DM-induced inflammation levels and markedly promoted hippocampal neurogenesis and memory function. However, these positive effects were reversed by 10 weeks of treatment with cyclo RGDyk, an inhibitor of irisin receptor signaling. Additionally, exercise helped reduce the M1 phenotype polarization of hippocampal microglia in diabetic mice; this effect could be reversed with cyclo RGDyk treatment. Moreover, exercise markedly increased the levels of fibronectin type III domain-containing protein 5 (FNDC5)/irisin protein while decreasing the expression of Toll-like receptor 4 (TLR4), myeloid differential protein-88 (MyD88), and nuclear factor kappa-B (NF-κB) in the hippocampus of T2DM mice. However, blocking irisin receptor signaling counteracted the down-regulation of TLR4/MyD88/NF-κB in diabetic mice undergoing exercise intervention. Conclusively, exercise appears to be effective in reducing neuroinflammation and enhancing hippocampal neurogenesis and memory in diabetes mice. The positive effects are involved in the participation of the irisin/TLR4/MyD88/NF-κB signaling pathway, highlighting the potential of exercise in the management of diabetic-induced cognitive decline. Full article
(This article belongs to the Special Issue Animal Models of Neurodegenerative Diseases)
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Figure 1

Figure 1
<p>Timeline of the experimental procedures and the impact of exercise on hippocampal neurogenesis in diabetic mice. (<b>A</b>) The schematic depicts the timeline of the experimental procedures and the organization of the experimental groups. The mice were injected with streptozotocin (STZ) or sodium citrate buffer at 8 weeks of age to induce T2DM. After the induction of T2DM, the mice were allocated to the following groups: control (n = 8), diabetes (n = 8), diabetes with exercise (n = 8), and diabetes with exercise plus cyclo RGDyk treatment (n = 8). The regimen of treadmill exercise and cyclo RGDyk administration was maintained over 10 weeks. At the end of the experiment, all groups underwent the Morris water maze test and were then used for immunofluorescence and other analyses. (<b>B</b>) Western blot imagery and quantitative analysis of Iba1 expression in the hippocampus (n = 6). (<b>C</b>) Representative confocal images of DCX (red) and DAPI (blue) staining in the hippocampus, accompanied by the area of positive area expression of DCX (%) (n = 6). Scale bar = 500 µm. (<b>D</b>) The swimming trajectories and the number of platform crossings in the Morris water maze test (n = 8). (<b>E</b>) Escape latency (n = 8). (<b>F</b>) The total distance traveled (n = 8). (<b>G</b>) Swimming speed (n = 8). Data are represented as ± SD; * denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the CON group; # denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the DM group; <span>$</span> and &amp; denote significance (<span class="html-italic">p</span> &lt; 0.05) between day 6 and day 1.</p>
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<p>The impact of exercise on hippocampal neuroinflammation in diabetes. (<b>A</b>) Representative Western blot images of Iba1, IL-1β, and TNF-α expression in the hippocampus. (<b>B</b>–<b>D</b>) Quantification of Iba1, IL-1β, and TNF-α expression in the hippocampus of the mice. (<b>E</b>,<b>F</b>) Relative mRNA levels of TNF-α and IL-1β in the hippocampus of the mice. (<b>G</b>) Representative confocal images of Iba1 (green) and DAPI (blue) staining in the hippocampus, accompanied by the area of positive area expression of Iba1 (%). Scale bar = 500 µm. n = 6 mice per group. Data are represented as ± SD. * denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the Con group. # denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the DM group.</p>
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<p>The beneficial impact of exercise-induced irisin on the polarization of microglial cells in the diabetic hippocampus. (<b>A</b>) Representative confocal images of Iba1 (green), iNOS (magenta), and DAPI (blue) staining in the hippocampus, along with the count of co-localized positive cells. Scale bar = 500 µm. (<b>B</b>) Representative confocal images of Iba1 (green), CD206 (magenta), and DAPI (blue) staining in the hippocampus, along with the count of co-localized positive cells. Scale bar = 500 µm. n = 6 mice per group. Data are represented as ± SD. * denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the CON group, # denotes significance (<span class="html-italic">p</span> &lt; 0.05) compared to the DM group.</p>
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<p>Injection of cyclo RGDyk reverses the beneficial effects of exercise-induced irisin on the hippocampus in diabetes. (<b>A</b>) Representative Western blot images of FNDC5, TLR4, MyD88, and p-NF-κB/NF-κB expression in the hippocampus. (<b>B</b>–<b>E</b>) Quantification of FNDC5, TLR4, MyD88, and p-NF-κB/NF-κB expression in the hippocampus of the mice. Data are represented as ± SD. n = 6 mice per group. * indicate significance (<span class="html-italic">p</span> &lt; 0.05) compared to the CON group, # denote significance (<span class="html-italic">p</span> &lt; 0.05) compared to the DM group, and + denote signify significance (<span class="html-italic">p</span> &lt; 0.05) compared to the Ex group.</p>
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<p>The injection of cyclo RGDyk counteracts the beneficial effects of exercise-induced irisin on microglial cells and adult neurogenesis in the hippocampus of diabetic mice. (<b>A</b>) Escape latency (n = 8). (<b>B</b>) The total swimming distance from four water maze tests each day for the first six days. (n = 8). (<b>C</b>) Swimming speed (n = 8). (<b>D</b>) The number of platform crossings in the Morris water maze test (n = 8). (<b>E</b>) Representative Western blot images of Iba1 and DCX expression in the hippocampus (n = 6). (<b>F</b>,<b>H</b>) Representative confocal images of Iba1 (green) and DAPI (blue) staining in the hippocampus, accompanied by the area of positive area expression of Iba1 (%) (n = 6). Scale bar = 500 µm. (<b>G</b>,<b>I</b>) Representative confocal images of DCX (red) and DAPI (blue) staining in the hippocampus, accompanied by the area of positive area expression of DCX (%) (n = 6). Scale bar = 500 µm. (<b>D</b>,<b>E</b>) Quantification of Iba1 and DCX expression in the hippocampus of the mice (n = 6). (<b>J</b>) Representative confocal images of Iba1 (green), CD206 (magenta), and DAPI (blue) staining in the hippocampus, along with the count of co-localized positive cells (n = 6). (<b>K</b>) Representative confocal images of Iba1 (green), iNOS (magenta), and DAPI (blue) staining in the hippocampus, along with the count of co-localized positive cells (n = 6). Data are represented as ± SD. + denote significance (<span class="html-italic">p</span> &lt; 0.05) compared to the Ex group.</p>
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14 pages, 8675 KiB  
Brief Report
TLR4 Downregulation Identifies High-Risk HPV Infection and Integration in H-SIL and Squamous Cell Carcinomas of the Uterine Cervix
by Angela Santoro, Giuseppe Angelico, Damiano Arciuolo, Giulia Scaglione, Belen Padial Urtueta, Gabriella Aquino, Noemy Starita, Maria Lina Tornesello, Rosalia Anna Rega, Maria Carmela Pedicillo, Manuel Mazzucchelli, Ilenia Sara De Stefano, Rosanna Zamparese, Giuseppina Campisi, Giorgio Mori, Gian Franco Zannoni and Giuseppe Pannone
Curr. Issues Mol. Biol. 2024, 46(10), 11282-11295; https://doi.org/10.3390/cimb46100670 - 10 Oct 2024
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Abstract
Growing scientific evidence suggests a link between the expression of toll-like receptor 4 (TLR4) and cervical cancer carcinogenesis. Specifically, a close relation between TLR4 expression and FIGO stage, lymph node metastases, and tumor size has been reported in cervical cancer. In the present [...] Read more.
Growing scientific evidence suggests a link between the expression of toll-like receptor 4 (TLR4) and cervical cancer carcinogenesis. Specifically, a close relation between TLR4 expression and FIGO stage, lymph node metastases, and tumor size has been reported in cervical cancer. In the present study, we aimed to evaluate the relationship between TLR4 expression levels and human papillomavirus (HPV) infection and/or high-risk (hr) HPV integration status in patients with a histological diagnosis of high-grade squamous intraepithelial lesion (H-SIL), and squamous cell carcinoma (SCC) of the uterine cervix. Sixty biopsies of cervical neoplasia, comprising H-SIL (n = 20) and SCC (n = 40), were evaluated for TLR4 expression by immunohistochemistry. All samples were positive for high-risk HPV as confirmed by in situ hybridization (ISH) and broad-spectrum PCR followed by Sanger sequencing analysis. The intensity of TLR4 staining was higher in tissues negative for intraepithelial lesion or malignancy (NILM) than in H-SIL, and further reduced in SCC. Moreover, statistically significant differences have been observed in the percentage of TLR4 expression between NILM and H-SIL and between H-SIL and SCC, with higher percentages of expression in H-SIL than in SCC. Our results showed a significant downregulation of TLR4 in HPV-related H-SIL and SCC, compared to NILM. These data support the hypothesis that TLR4 expression is suppressed in HPV-driven oncogenesis. Full article
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<p>TLR4 expression intensity and percentage. The analysis of TLR4 SI in NILM, H-SIL, and SCC samples showed a statistically significant difference between the 3 groups.</p>
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<p>(<b>A</b>) NILM showing negative immunostaining for TLR4. (<b>B</b>) H-SIL showing diffuse and intense immunostaining for TLR4.</p>
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<p>TLR4 staining patterns. (<b>A</b>,<b>B</b>) Double immunostaining for p16 (DAB–brown) and TLR-4 (Fast Red) in SCC. With double immunostaining, p16 staining was distributed at the periphery of the tumoral nests while TLR4 was more evident centrally, in more differentiated tumoral cells. (<b>C</b>) A case of H-SIL showing low TLR-4 expression (downregulation) is depicted. (<b>D</b>) A case of SCC showing moderate TLR4 expression.</p>
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<p>Immunohistochemical expression of CK19. The analysis of percentages of immunohistochemical expression of CK19 showed a statistically significative difference between normal epithelium, H-SIL, and SCC (<span class="html-italic">p</span> &lt; 0.05; ANOVA).</p>
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<p>Correlation expression of TLR4 and CK19. Inverse statistically significant correlation between TLR4-SI and CK19 percentage of expressions (R = −0.4906; <span class="html-italic">p</span> = 0.0001, ANOVA), with CK19 upregulation and TLR4 downregulation in the cervical carcinogenetic process.</p>
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<p>CK19 upregulation and TLR4 downregulation in the cervical carcinogenetic process. (<b>A</b>) Immunohistochemical expression of CK19 SCC; (<b>B</b>) immunohistochemical expression of TLR4 in the same case of SCC.</p>
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<p>Correlation between intensity of TLR-4 expression and the degree of inflammatory infiltration. A statistically significant correlation between TLR4 downregulation and inflammatory tumoral microenvironment has been demonstrated (R = 0.5341; <span class="html-italic">p</span> = 0.001, ANOVA).</p>
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