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Search Results (1,442)

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20 pages, 1991 KiB  
Review
Mucosal Inflammatory Memory in Chronic Rhinosinusitis
by Min-Seok Koo, Sungmin Moon and Min-Seok Rha
Cells 2024, 13(23), 1947; https://doi.org/10.3390/cells13231947 - 23 Nov 2024
Viewed by 196
Abstract
Recent advancements in medical management, endoscopic sinus surgery, and biologics have significantly improved outcomes for patients with chronic rhinosinusitis (CRS). However, long-term recurrence is frequently observed following endoscopic sinus surgery, with symptoms worsening after biologics are discontinued. Consequently, refractory or recurrent CRS remains [...] Read more.
Recent advancements in medical management, endoscopic sinus surgery, and biologics have significantly improved outcomes for patients with chronic rhinosinusitis (CRS). However, long-term recurrence is frequently observed following endoscopic sinus surgery, with symptoms worsening after biologics are discontinued. Consequently, refractory or recurrent CRS remains a significant challenge, causing a substantial healthcare burden. In this review, we provide current insights into mucosal inflammatory memory, a potential mechanism leading to CRS recurrence. Given that both immune and non-immune cells in the sinonasal mucosa play critical roles in the pathophysiology of CRS, a deeper understanding of the mechanisms underlying mucosal inflammatory memory in various cellular components of sinonasal tissue could aid in the management of refractory CRS. We describe and discuss the latest knowledge regarding the novel concept of inflammatory memory, including both adaptive immune memory and trained immunity. Additionally, we summarize the pathogenic memory features of the sinonasal mucosa cellular components in the context of CRS. Full article
16 pages, 3844 KiB  
Article
Metagenomics Reveals Sex-Based Differences in Murine Fecal Microbiota Profiles Induced by Chronic Alcohol Consumption
by Manuel Domínguez-Pino, Susana Mellado, Carlos M. Cuesta, Rubén Grillo-Risco, Francisco García-García and María Pascual
Int. J. Mol. Sci. 2024, 25(23), 12534; https://doi.org/10.3390/ijms252312534 - 22 Nov 2024
Viewed by 250
Abstract
Chronic ethanol exposure induces an inflammatory response within the intestinal tract, compromising mucosal and epithelial integrity and leading to dysbiosis of the gut microbiome. However, the specific roles of the gut microbiota in mediating ethanol-induced effects, as well as their interactions with the [...] Read more.
Chronic ethanol exposure induces an inflammatory response within the intestinal tract, compromising mucosal and epithelial integrity and leading to dysbiosis of the gut microbiome. However, the specific roles of the gut microbiota in mediating ethanol-induced effects, as well as their interactions with the immune system, remain poorly characterized. This study aimed to evaluate sex-based differences in fecal microbiota profiles induced by chronic alcohol consumption and to assess whether TLR4 is involved in these effects. We analyzed the 16S rRNA gene sequencing of fecal samples from male and female wild-type (WT) and TLR4-knockout (TLR4-KO) mice with and without chronic ethanol exposure over a three-month period. Our findings provide evidence, for the first time, that male mice are more susceptible to the effects of ethanol on the fecal microbiota, since ethanol exposure induced greater alterations in the Gram-negative and -positive bacteria with immunogenic capacity in the WT male mice than in the female mice. We also demonstrate that the absence of immune receptor TLR4 leads to different microbiota in both sexes, showing anti-inflammatory and protective properties for intestinal barrier function and resulting in a phenotype more resistant to ethanol’s effects. These findings may open new avenues for understanding the relationship between gut microbiota profiles and inflammation in the digestive system induced by chronic alcohol consumption. Full article
(This article belongs to the Special Issue New Insights into Gut Microbiota and Immunity)
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Graphical abstract

Graphical abstract
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<p>Alpha and beta diversity in the fecal microbiota of the WT and TLR4-KO mice with chronic alcohol exposure. (<b>A</b>) Alpha diversity was measured to assess the microbial richness and evenness within each sample group using the Chao1, Shannon, and Simpson indices. The Chao1 index estimated species richness by including rare species. The Shannon index measured both richness and evenness, providing valuable insight into species uniformity across our samples. The Simpson index complemented the Shannon by emphasizing the dominant species, allowing us to observe whether specific taxa were disproportionately abundant due to treatment or genotype differences. (<b>B</b>) A beta diversity plot based on Bray–Curtis distances using non-metric multidimensional scaling (NMDS) was used to visualize the differences in the microbiota community compositions among the experimental groups based on Bray–Curtis dissimilarities, associated with both the presence/absence and abundance of taxa. Each point represents a sample’s microbial profile, and the clustering indicates similarities in the microbial communities between the samples. The PERMANOVA (F-value: 3.0107, R-squared: 0.3238, <span class="html-italic">p</span>-value: 0.00099) was used to statistically assess the group clustering.</p>
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<p>The relative species abundance in the fecal samples at the (<b>A</b>) phylum level, (<b>B</b>) family level, and (<b>C</b>) genus level.</p>
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<p>Summary of the differential abundance analysis at the genus level. (<b>A</b>) Results of the comparison between the ethanol-treated WT male and female mice (F-WT-Et vs. M-WT-Et). (<b>B</b>) Results of the comparison between the ethanol-treated and untreated WT female mice (F-WT-Et vs. F-WT). (<b>C</b>) Results of the comparison between the ethanol-treated and untreated WT male mice (M-WT-Et vs. M-WT). The positive log2 fold changes indicated an over-representation of the taxa in the first group compared to the second, while the negative log2 fold changes indicated an under-representation of the taxa in the first group compared to the second. The dot colors represent the phylum corresponding to each genus. Only the ASVs with an adjusted <span class="html-italic">p</span>-value of &lt;0.05 are shown.</p>
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<p>Summary of the differential abundance analysis at the genus level. (<b>A</b>) Results of the comparison between the ethanol-treated WT and TLR4-KO female mice (F-WT-Et vs. F-KO-Et). (<b>B</b>) Results of the comparison between the ethanol-treated WT and TLR4-KO male mice (M-WT-Et vs. M-KO-Et). The positive log2 fold changes indicated an over-representation of the taxa in the first group compared to the second, while the negative log2 fold changes indicated an under-representation of the taxa in the first group compared to the second. The dot colors represent the phylum corresponding to each genus. Only the ASVs with an adjusted <span class="html-italic">p</span>-value of &lt;0.05 are shown.</p>
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<p>Schematic workflow of the experimental design and analysis. (<b>A</b>) Female and male C57BL/6J mice, with and without TLR4 deletion, were either administered water containing 10% (<span class="html-italic">v</span>/<span class="html-italic">v</span>) ethanol at the 2 month time point or maintained as parallel untreated controls. Fecal samples were collected from all mice at 5 months (3 months of ethanol treatment). DNA was extracted from the feces, and genomic 16S rRNA was sequenced. (<b>B</b>) The 16S rRNA gene sequencing analysis. (<b>C</b>) Bar graphs representing the body weights of all animals at the end of the ethanol treatment, along with the average daily food and liquid intake over the 3 months of ethanol treatment. The data represent means ± SEMs, with n = 6–8 mice/group.</p>
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12 pages, 269 KiB  
Review
Importance of Lactobacilli for Human Health
by Piotr B. Heczko, Milena Giemza, Weronika Ponikiewska and Magdalena Strus
Microorganisms 2024, 12(12), 2382; https://doi.org/10.3390/microorganisms12122382 - 21 Nov 2024
Viewed by 416
Abstract
As an extraordinarily diverse group of bacteria, lactobacilli are now classified into several genera, many of which still include “Lactobacillus” in their names. Despite their names, this group of lactic acid bacteria comprises microorganisms that are crucial for human health, especially during the [...] Read more.
As an extraordinarily diverse group of bacteria, lactobacilli are now classified into several genera, many of which still include “Lactobacillus” in their names. Despite their names, this group of lactic acid bacteria comprises microorganisms that are crucial for human health, especially during the early development of the human microbiota and immune system. The interactions between lactobacilli and components of the mucosal immunity lead to its shaping and development, which is possibly considered a prime mover in the advancement of the human immune system. Although much of the evidence backing the pivotal role of lactobacilli in maintaining human health comes from studies on probiotics aiming to elucidate the mechanisms of their functional activities and studies on mucosal immunity in germ-free mice, it is justifiable to extend observations on the properties of the individual probiotic Lactobacillus that are related to health benefits onto other strains sharing common characteristics of the species. In this review, we will discuss the acquisition, presence, and functions of lactobacilli in different human microbiota throughout their whole life, including those arising in the amnion and their interactions with mucosal and immune cells. Examples of immune system modulation by probiotic lactobacilli include their colonic competition for available nutrients, interference with colonization sites, competition for binding sites on gut epithelial cells, bacteriocin production, reduction of colonic pH, and nonspecific stimulation of the immune system. Full article
(This article belongs to the Special Issue Probiotic and Postbiotic Properties of Lactobacillus)
14 pages, 2430 KiB  
Article
Preliminary Study on Type I Interferon as a Mucosal Adjuvant for Human Respiratory Syncytial Virus F Protein
by Hongqiao Hu, Li Zhang, Lei Cao, Jie Jiang, Yuqing Shi, Hong Guo, Yang Wang, Hai Li and Yan Zhang
Vaccines 2024, 12(11), 1297; https://doi.org/10.3390/vaccines12111297 - 20 Nov 2024
Viewed by 394
Abstract
Background: Human respiratory syncytial virus (HRSV) imposes a significant disease burden on infants and the elderly. Intranasal immunization using attenuated live vaccines and certain vector vaccines against HRSV has completed phase II clinical trials with good safety and efficacy.Recombinant protein vaccines for mucosal [...] Read more.
Background: Human respiratory syncytial virus (HRSV) imposes a significant disease burden on infants and the elderly. Intranasal immunization using attenuated live vaccines and certain vector vaccines against HRSV has completed phase II clinical trials with good safety and efficacy.Recombinant protein vaccines for mucosal immunization require potent mucosal adjuvants. Type I interferon (IFN), as a natural mucosal adjuvant, significantly enhances antigen-presenting cell processing and antigen presentation, promoting the production of T and B cells. Methods: This study utilized human α2b interferon (IFN-human) and mouse α2 interferon (IFN-mouse) as nasal mucosal adjuvants in combination with fusion protein (F). Intranasal immunization was performed on BALB/c mice to evaluate the immunogenicity of the formulation in vivo. Results: Compared to the F protein immunization group, mice in the F + IFN-Human and F + IFN-Mouse experimental groups exhibited significantly increased neutralizing antibody titers and augmented secretion of IFN-γ and IL-4 by lymphocytes,  and both of them could induce the production of high-titer specific IgA antibodies in mice (p < 0.001).The F + IFN-Human immunization induced the highest IgG and IgG1 antibody titers in mice; however, the F + IFN-Mouse immunization group elicited the highest neutralizing antibody titers (598), lowest viral loads in the lungs (Ct value of 31), and fastest weight recovery in mice. Moreover, mice in the F + IFN-Mouse immunization group displayed the mildest lung pathological damage (Total score of pathological injury was 2). Conclusions: In conclusion, IFN-Mouse, as a mucosal adjuvant for HRSV recombinant protein vaccines, demonstrated superior protective effects in mice compared to IFN-Human adjuvants. Full article
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Figure 1

Figure 1
<p>Schematic diagram of HEK293 cells expressing two IFN proteins. SDS-PAGE and Western blot were used to confirm that the protein sizes of (<b>A</b>) IFN-Mouse and (<b>B</b>) IFN-Human were accurate.</p>
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<p>Humoral and cellular immune responses were induced in mice after immunization (<b>A</b>): Serum-specific IgG antibody level after immunization. The data represent the geometric means ± SD. LD indicates the limit of detection, which is half of the lowest dilution of serum, and for this experiment, LD = 50. (<b>B</b>) The level of serum-neutralizing antibody after immunization: The data represent the geometric means ± SD. LD indicates the limit of detection, which is half of the lowest dilution of serum, and for this experiment, LD = 8. (<b>C</b>) The number of cytokines that induce mice spleen lymphocytes to secrete IFN-γ after immunization. (<b>D</b>) Cytokine number of IL-4 secreted by spleen lymphocytes of mice after immunization: The data of cytokine secreting cells represent the difference between the number of spots per 3 × 10<sup>5</sup> cells in the Pre F stimulation hole and the number of spots per 3 × 10<sup>5</sup> cells in the medium treatment hole. Data represent average ± SD. Statistically significant differences were measured by appropriate one-way ANOVA (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001). For the experimental data in this study, each group was set up with replicates to ensure the reproducibility of the results, and the average values were used for subsequent statistical analysis. Data represent two independent experiments.</p>
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<p>Bias of induced immune response in mice after immunization: (<b>A</b>) The antibody level of serum specific IgG 1 after immunization; (<b>B</b>) the serum specific IgG 2a antibody level of mice after immunization. The data represent the geometric means ± SD, LD indicates the limit of detection, which is half of the lowest dilution of serum, and for this experiment, LD = 50; (<b>C</b>) the ratio of serum-specific antibody IgG 1 to IgG 2a after immunization; (<b>D</b>) the ratio of cytokine IL-4/IFN-γ secreted by spleen lymphocytes of mice after immunization; statistically significant differences were measured by appropriate one-way ANOVA (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Changes in IgA antibody titers, viral lung load, and body weight in mice after challenge: (<b>A</b>) Serum-specific IgA antibody level of mice after 4d challenge. The data represent the geometric means ± SD, LD indicates the limit of detection, which is half of the lowest dilution of serum, and for this experiment, LD = 10; (<b>B</b>) CT values of viral lung load of mice after 4d challenge. The data represent the geometric means ± SD; (<b>C</b>) changes in body weight of mice after 4d challenge. The average relative body weight ± SEM of all mice in each group (compared with control group); one-way ANOVA analysis was performed using the one-way ANOVA function in GraphPad Prism, followed by Tukey’s multiple comparison test to further analyze the differences between different groups on the same day post-infection (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001), and the color indicates the group.</p>
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<p>Degree of pathological injury to the lung in mice after challenge: (<b>A</b>) pathological sections of mouse lungs after challenge; (<b>B</b>) total score of pathological injury to the lungs of mice after challenge, * <span class="html-italic">p</span> &lt; 0.05.</p>
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21 pages, 7032 KiB  
Article
Modulation of Canine Gut Microbiota by Prebiotic and Probiotic Supplements: A Long-Term In Vitro Study Using a Novel Colonic Fermentation Model
by Alessandro Gramenzi, Luana Clerico, Benedetta Belà, Meri Di Leonardo, Isa Fusaro and Giulia Pignataro
Animals 2024, 14(22), 3342; https://doi.org/10.3390/ani14223342 - 20 Nov 2024
Viewed by 224
Abstract
The gut microbiota plays a crucial role in dogs’ health, influencing immune function, digestion, and protection against pathogens. This study evaluates the effects of three canine dietary supplements—Microbiotal (prebiotic), Lactobacillus reuteri (probiotic), and a combination of both—on the gut microbiota composition of a [...] Read more.
The gut microbiota plays a crucial role in dogs’ health, influencing immune function, digestion, and protection against pathogens. This study evaluates the effects of three canine dietary supplements—Microbiotal (prebiotic), Lactobacillus reuteri (probiotic), and a combination of both—on the gut microbiota composition of a healthy canine donor using an in vitro colonic fermentation model. The SCIME™ platform, adapted to simulate the canine gastrointestinal tract, was used to monitor microbial shifts in the luminal and mucosal environments of the proximal and distal colon over a 2-week treatment period. The microbial communities were analyzed using 16S rRNA sequencing to assess changes at various taxonomic levels. Alpha- and beta-diversity indices were calculated, while LEfSe and treeclimbR were employed to identify taxa-driving microbial shifts. Results indicated that all treatments led to significant modulations in key microbial groups, with enrichment of Limosilactobacillus, Bifidobacterium, Prevotella, and Faecalibacterium. These changes suggest improved saccharolytic fermentation and butyrate production, particularly when prebiotics and probiotics were co-administered. This study highlights the promising benefits of combined prebiotic and probiotic supplementation in promoting gut health and microbial diversity, providing a basis for future studies targeting the metabolic activity of the gut microbiota using the same supplements and technology. Full article
(This article belongs to the Section Companion Animals)
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Figure 1

Figure 1
<p>Standard setup of the Simulator of the Canine Intestinal Microbial Ecosystem (SCIME™), consisting of four sequential reactors, simulating the different canine gastrointestinal tract regions.</p>
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<p>Modified version of the SCIME into a Triple-M-SCIME used for the current study. St + SI: vessel serving as stomach and small intestine, PC: proximal colon, and DC: distal colon.</p>
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<p>Effect of treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) on alpha diversity as calculated using four different measures (observed (count of unique taxa in each sample), Chao1, Shannon, and Simpson) in the luminal proximal colon (PC) at the end of the control (CTRL) and treatment (TR) period. Three samples (A, B, C) were collected during each period, represented by different colors.</p>
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<p>Effect of treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) on alpha diversity as calculated using four different measures (observed (count of unique taxa in each sample), Chao1, Shannon, and Simpson) in the luminal distal colon (DC) at the end of the control (CTRL) and treatment (TR) period. Three samples (A, B, C) were collected during each period, represented by different colors.</p>
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<p>Effect of treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) on alpha diversity as calculated using four different measures (observed (count of unique taxa in each sample), Chao1, Shannon, and Simpson) in the mucosal proximal colon (PC) at the end of the control (CTRL) and treatment (TR) period. Three samples (A, B, C) were collected during each period, represented by different colors.</p>
Full article ">Figure 6
<p>Effect of treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) on alpha diversity as calculated using four different measures (observed (count of unique taxa in each sample), Chao1, Shannon, and Simpson) in the mucosal distal colon (DC) at the end of the control (CTRL) and treatment (TR) period. Three samples (A, B, C) were collected during each period, represented by different colors.</p>
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<p>Discriminant analysis of principal components (DAPC) to show differences in community composition (beta diversity) in the luminal proximal colon (PC) at the end of the control (CTRL) and treatment (TR) period following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P). Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3). Each dot represents one sample.</p>
Full article ">Figure 8
<p>Discriminant analysis of principal components (DAPC) to show differences in community composition (beta diversity) in the luminal distal colon (DC) at the end of the control (CTRL) and treatment (TR) period following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P). Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3). Each dot represents one sample.</p>
Full article ">Figure 9
<p>Discriminant analysis of principal components (DAPC) to show differences in community composition (beta diversity) in the mucosal proximal colon (PC) at the end of the control (CTRL) and treatment (TR) period following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P). Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3). Each dot represents one sample.</p>
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<p>Discriminant analysis of principal components (DAPC) to show differences in community composition (beta diversity) in the mucosal distal colon (DC) at the end of the control (CTRL) and treatment (TR) period following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P). Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3). Each dot represents one sample.</p>
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<p>Jitter plots showing abundances of different phyla in the luminal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 12
<p>Jitter plots showing abundances of different phyla in the luminal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 13
<p>Jitter plots showing abundances of different phyla in the mucosal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 14
<p>Jitter plots showing abundances of different phyla in the mucosal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
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<p>Jitter plots showing abundances of the 20 most abundant families in the luminal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 16
<p>Jitter plots showing abundances of the 20 most abundant families in the luminal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 17
<p>Jitter plots showing abundances of the 20 most abundant families in the mucosal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 18
<p>Jitter plots showing abundances of the 20 most abundant families in the mucosal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 19
<p>Jitter plots showing abundances of the 20 most abundant genera in the luminal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 20
<p>Jitter plots showing abundances of the 20 most abundant genera in the luminal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on absolute levels. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 21
<p>Jitter plots showing abundances of the 20 most abundant genera in the mucosal proximal colon (PC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
Full article ">Figure 22
<p>Jitter plots showing abundances of the 20 most abundant genera in the mucosal distal colon (DC) following treatment with the different test products (Microbiotal, M; probiotic, P; and their combination, M + P) at the end of the control (CTRL) and treatment (TR) period based on relative abundances. Each color represents one of six categories (groups), i.e., CTRL_M (n = 3), TR_M (n = 3), CTRL_P (n = 3), TR_P (n = 3), CTRL_M + P (n = 3), and TR_M + P (n = 3).</p>
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20 pages, 4135 KiB  
Review
Microbiome Shifts and Their Impact on Gut Physiology in Irritable Bowel Syndrome
by Ioanna Aggeletopoulou and Christos Triantos
Int. J. Mol. Sci. 2024, 25(22), 12395; https://doi.org/10.3390/ijms252212395 - 19 Nov 2024
Viewed by 425
Abstract
Irritable bowel syndrome (IBS) is one of the most prevalent functional gastrointestinal disorders characterized by recurrent abdominal pain and altered bowel habits. The exact pathophysiological mechanisms for IBS development are not completely understood. Several factors, including genetic predisposition, environmental and psychological influences, low-grade [...] Read more.
Irritable bowel syndrome (IBS) is one of the most prevalent functional gastrointestinal disorders characterized by recurrent abdominal pain and altered bowel habits. The exact pathophysiological mechanisms for IBS development are not completely understood. Several factors, including genetic predisposition, environmental and psychological influences, low-grade inflammation, alterations in gastrointestinal motility, and dietary habits, have been implicated in the pathophysiology of the disorder. Additionally, emerging evidence highlights the role of gut microbiota in the pathophysiology of IBS. This review aims to thoroughly investigate how alterations in the gut microbiota impact physiological functions such as the brain–gut axis, immune system activation, mucosal inflammation, gut permeability, and intestinal motility. Our research focuses on the dynamic “microbiome shifts”, emphasizing the enrichment or depletion of specific bacterial taxa in IBS and their profound impact on disease progression and pathology. The data indicated that specific bacterial populations are implicated in IBS, including reductions in beneficial species such as Lactobacillus and Bifidobacterium, along with increases in potentially harmful bacteria like Firmicutes and Proteobacteria. Emphasis is placed on the imperative need for further research to delineate the role of specific microbiome alterations and their potential as therapeutic targets, providing new insights into personalized treatments for IBS. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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Figure 1

Figure 1
<p>Microbiome shifts in irritable bowel syndrome. Created with BioRender.com (accessed on 14 November 2024).</p>
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<p>Microbiome–Gut–Brain axis interactions and their role in irritable bowel syndrome pathophysiology. <a href="#ijms-25-12395-f002" class="html-fig">Figure 2</a> illustrates the complex interplay between microbiome alterations, gut physiology, immune system activation, and neuroendocrine pathways in the context of irritable bowel syndrome (IBS). At the intestinal lumen, food particles are broken down, releasing various metabolites such as short-chain fatty acids (SCFAs), bile acids, and indoles. These metabolites, alongside lipopolysaccharides (LPS) from disrupted gut bacteria, modulate intestinal permeability by affecting tight junctions and enterocyte integrity. The immune system of the intestinal tissue is activated by this microbial translocation, leading to the secretion of proinflammatory cytokines by macrophages, dendritic cells (DCs), and T cells. These cytokines further perpetuate immune activation, mast cell degranulation (releasing histamine and proteases), and enteric glial cell stimulation. The activation of the immune cells and the release of neurotransmitters result in sensitization of enteric neurons, contributing to visceral hypersensitivity, a hallmark of IBS symptoms. Serotonin (5-HT), primarily released by enteroendocrine cells, alongside glucagon-like peptide-1 (GLP-1), influences gut motility and sensory signaling through enteric neurons. Impaired tight junctions allow further interactions between gut metabolites like indoles and the immune system, exacerbating gut–brain axis dysregulation. The hypothalamic–pituitary–adrenal (HPA) axis is shown to be impaired, with stress playing a critical role in increasing cortisol levels, which regulates both immune responses and the central nervous system. This feedback loop involving stress and immune mediators contributes to increased visceral hypersensitivity and IBS symptoms through vagus nerve signaling and neurotransmitter alterations. Created with BioRender.com (accessed on 1 November 2024). Abbreviations: SCFAs, short-chain fatty acids; LPS, lipopolysaccharides; 5-HT, serotonin; DC, dendritic cell; TLR, toll-like receptor; GLP-1, glucagon-like peptide-1; HPA, hypothalamic–pituitary–adrenal.</p>
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17 pages, 1871 KiB  
Review
Breaching the Barrier: Investigating Initial Herpes Simplex Viral Infection and Spread in Human Skin and Mucosa
by Hafsa Rana, Naomi R. Truong, Dona R. Sirimanne and Anthony L. Cunningham
Viruses 2024, 16(11), 1790; https://doi.org/10.3390/v16111790 - 18 Nov 2024
Viewed by 454
Abstract
Herpes simplex virus (HSV) is sexually transmitted via the anogenital mucosa where it initially infects epidermal keratinocytes and mononuclear phagocytes (MNPs). It then spreads to the dorsal root ganglion via sensory nerve endings, to remain latent for life with periodic reactivation. Currently, there [...] Read more.
Herpes simplex virus (HSV) is sexually transmitted via the anogenital mucosa where it initially infects epidermal keratinocytes and mononuclear phagocytes (MNPs). It then spreads to the dorsal root ganglion via sensory nerve endings, to remain latent for life with periodic reactivation. Currently, there is no cure or vaccine. Initial or recurrent HSV infection can produce serious complications and mediate acquisition of HIV. This review outlines the initial events after the HSV infection of human anogenital mucosa to determine the optimal window to target the virus before it becomes latent. After infection, HSV spreads rapidly within the mid-layers of epidermal keratinocytes in the explanted human inner foreskin. Infected cells produce chemokines, which modulate nectin-1 distribution on the surface of adjacent keratinocytes, facilitating viral spread. Epidermal Langerhans cells and dendritic cells become infected with HSV followed by a “viral relay” to dermal MNPs, which then present viral antigen to T cells in the dermis or lymph nodes. These data indicate the need for interruption of spread within 24 h by diffusible vaccine-induced mediators such as antiviral cytokines from resident immune cells or antibodies. Intradermal/mucosal vaccines would need to target the relevant dermal MNPs to induce HSV-specific CD4+ and CD8+ T cells. Full article
(This article belongs to the Special Issue Innate and Adaptive Immunity to Cutaneous Virus Infection)
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<p>Differences in mucosal tissue types. (<b>A</b>) Haematoxylin and eosin stain of layers of skin (epidermis/dermis) and type II mucosa (epithelium/lamina propria). (<b>B</b>) Schematic diagram summarising the different locations and compositions of skin and keratinised/non-keratinised type II mucosae. Skin covers the exposed surfaces and has a thick layer of keratinization, which changes according to anatomical site. Inner foreskin is a type of type II mucosa, with keratinisation and slightly more stratification, whereas vagina and oral mucosae have a highly stratified epithelium and usually are not keratinized. Keratinisation can occur on the vagina in the follicular phase of the menstrual cycle. Created on <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>HSV infection of keratinocytes in human anogenital epidermis. Schematic diagram summarising the initial events during HSV infection of keratinocytes. Keratinocytes are infected via HSV entry receptor, nectin-1. Productive infection leads to secretion of cytokines and chemokines such as IL-6, IL-8, CXCL1, CXCL10, and CCL3. These cytokines and chemokines induce nectin-1 redistribution on neighbouring cells, promoting rapid viral spread to adjacent uninfected cells. Created on <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>HSV infection of mononuclear phagocytes in human anogenital epidermis. HSV infects Langerhans cells and epidermal dendritic cells (Epi DCs) in the epidermis. These cells apoptose and LCs (and probably Epi-DCs) migrate to the dermis, where they cluster with dermal mononuclear phagocyte (MNP) populations including types 1 and 2 of conventional DCs (cDC1s and cDC2s) and CD14<sup>+</sup> MNPs. These dermal MNPs may present to T cells at the site of infection or migrate to the lymph node for antigen presentation. Created on <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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18 pages, 5753 KiB  
Article
Mycoplasma bovis Invades Non-Phagocytic Cells by Clathrin-Dependent Endocytic Pathways and Escapes from Phagocytic Vesicles
by Bin Li, Yabin Lu, Yaru Feng, Xiaolong Jiao, Qiuyu Zhang, Mengting Zhou, Yuyu Zhang, Jian Xu, Yuefeng Chu and Duoliang Ran
Pathogens 2024, 13(11), 1003; https://doi.org/10.3390/pathogens13111003 - 15 Nov 2024
Viewed by 377
Abstract
Mycoplasma bovis (M. bovis) is capable of causing pneumonia, arthritis, mastitis, and various other ailments in cattle of all age groups, posing a significant threat to the healthy progression of the worldwide cattle industry. The invasion of non-phagocytic host cells serves [...] Read more.
Mycoplasma bovis (M. bovis) is capable of causing pneumonia, arthritis, mastitis, and various other ailments in cattle of all age groups, posing a significant threat to the healthy progression of the worldwide cattle industry. The invasion of non-phagocytic host cells serves as a pivotal mechanism enabling M. bovis to evade the immune system and penetrate mucosal barriers, thereby promoting its spread. To investigate the differences in M. bovis invasion into four types of non-phagocytic cells (Madin–Darby bovine kidney (MDBK) cells, embryonic bovine lung (EBL) cells, bovine embryo tracheal (EBTr) cells and bovine turbinate (BT) cells) and further elucidate its invasion mechanism, this study first optimized the experimental methods for M. bovis invasion into cells. Utilizing laser scanning confocal microscopy, transmission electron microscopy, and high-content live-cell imaging systems, the invasion process of M. bovis into four types of non-phagocytic cells was observed. The invasion rates of three different strains of M. bovis (PG45, 07801, 08M) were quantified through the plate counting method. In order to clarify the specific pathway of M. bovis invasion into cells, chlorpromazine (CPZ), amiloride (AMI), and methyl-β-cyclodextrin (M-β-CD) were used to inhibit CLR-mediated clathrin-dependent endocytosis (CDE) pathway, macropinocytosis, and lipid raft pathway, respectively. Subsequently, the invasion rates of PG45 into these four types of cells were measured. Using siRNA technology, the expression of clathrin (CLR) in EBL cells was knocked down to further verify the role of CLR in the invasion process of M. bovis. The results showed that the optimal conditions for M. bovis to invade non-phagocytic cells were a multiplicity of infection (MOI) of 1000 and an optimal invasion time of 4 h. All three strains of M. bovis have the ability to invade the four types of non-phagocytic cells, yet their invasion abilities vary significantly. Observations from transmission electron microscopy further confirmed that at 120 min post-infection, PG45 had successfully invaded EBL cells and was present within endocytic vesicles. It is noteworthy that almost all PG45 successfully escaped from the endocytic vesicles after 240 min of infection had passed. Through chemical inhibition experiments and CLR protein knockdown experiments, it was found that when the CDE and lipid raft pathways were blocked or CLR protein expression was reduced, the invasion rates of PG45, 07801, and 08M in MDBK, EBL, EBTr, and BT cells were significantly decreased (p < 0.05). The above results indicate that M. bovis can invade all types of non-phagocytic cells through endocytic pathways involving CDE (clathrin-dependent endocytosis) or lipid raft-mediated endocytosis, and possesses the ability to escape from phagosomes. Full article
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<p>Optimal selection of MBC and MOC. Different concentrations of Tetracycline, tiamulin, gentamicin, and azithromycin were added to the culture medium containing PG45. Tetracycline (<b>A</b>) and gentamicin (<b>C</b>) with a working concentration of 200 µg/mL to 400 µg/mL, as well as azithromycin (<b>D</b>) and tiamulin (<b>B</b>) with a working concentration of 400 µg/mL to 1000 µg/mL, can all effectively kill PG45. Under the condition of acting for 3 h, both gentamicin (<b>E</b>) and tiamulin (<b>F</b>) with a working concentration of 400 µg/mL can completely kill PG45. Different MOIs of PG 45 were added to EBL cells, when it increased to 100 of MOI, the invasion rate of PG45 began to be statistically significant (<b>G</b>). PG45 was added to EBL cells at an MOI of 1000, and the invasion rate of PG45 was measured by observing different interaction times (<b>H</b>). The cell morphology of PG45-infected EBL cells with an MOI of 1000 for 240 min, the morphology of EBL was essentially normal (100×) (<b>I</b>). After 240 min of infection with PG45 at an MOI of 10,000, EBL cells exhibited wrinkling and cytopathic effects (100×) (<b>J</b>).* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; NS, no significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Protocol and results of <span class="html-italic">M. bovis</span> intracellular survival test. (<b>A</b>) Intracellular viability assay protocol of <span class="html-italic">M. bovis</span>. (<b>B</b>) CFUs of extracellular PG45 after different culture periods: the number of <span class="html-italic">M. bovis</span> bacteria increased gradually with the passage of time, and there was a significant difference at 40 h compared to 32 h (<span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) CFU of intracellular PG45 after different culture periods: <span class="html-italic">M. bovis</span> had a significant difference at 32 h (<span class="html-italic">p</span> &lt; 0.05) and an extremely significant difference at 40 h and 48 h compared to 0 h (<span class="html-italic">p</span> &lt; 0.01). * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Invasion rates of four mycoplasma strains into various cell types. (<b>A</b>) PG45 exhibited the highest invasion rate in EBTr cells and the lowest in BT cells. (<b>B</b>) 08M. (<b>C</b>) 07801. (<b>D</b>) PG45-GFP. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; NS, no significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Invasion of four cell types by the PG45-GFP. (<b>A</b>) Laser confocal microscopy images of PG45-GFP strain invading MDBK, BT, EBL and EBTr cells. The red fluorescence represents the cytoplasm and cell membrane, the green fluorescence represents mycoplasma PG45-GFP, and the blue fluorescence represents the cell nucleus. (<b>B</b>) Quantification of intracellular green fluorescence intensity across the four cell types, revealing that the mean intracellular fluorescence intensity in EBTr cells was significantly higher compared to the other three cell types (<span class="html-italic">p</span> &lt; 0.01), while BT cells exhibited significantly lower mean intracellular fluorescence intensity (<span class="html-italic">p</span> &lt; 0.01). (<b>C</b>) Transmission electron microscopy images show PG45 invading various cells, with the white arrow indicating PG45 in the cytoplasm, the green arrow pointing to PG45 in endosomes, the red bidirectional arrow marking the cell membrane, and the yellow triangle denoting the nuclear membrane. *** <span class="html-italic">p</span> &lt; 0.001; NS, no significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>The process of PG45 invading EBL cells. (<b>A</b>) Laser confocal microscopy images capturing the invasion of EBL cells by PG45 at various time points. The red fluorescence represents the cytoplasm and cell membrane, the green fluorescence represents mycoplasma PG45-GFP, and the blue fluorescence represents the cell nucleus. (<b>B</b>) High-content video screenshots reveal that PG45 started adhering to the cell surface at 60 min, with significant adhesion and minor cytoplasmic entry at 90 min, which progressively increased over time. (<b>C</b>) Schematic diagram illustrating the intracellular positions of PG45, color-coded to show the gradual increase in PG45 over time. (<b>D</b>) 3D map of PG45 intrusion at 4 h, demonstrating the presence of green fluorescent PG45 within the yellow cytoplasm in all three views. (<b>E</b>–<b>J</b>) Sequential electron microscopy images showing the interaction between <span class="html-italic">M. bovis</span> and EBL cells: (<b>E</b>) Large numbers of <span class="html-italic">M. bovis</span> surrounding the cell membrane at 30 min; (<b>F</b>) PG45 beginning to bind tightly to the cell membrane at 60 min; (<b>G</b>) Cell membrane invagination induced by PG45 at 90 min; (<b>H</b>) PG45 entering the cell and residing in endosomes at 120 min; (<b>I</b>) PG45 located within intracellular vesicles at 180 min; (<b>J</b>) PG45 escaping from vesicles and residing in the cytoplasm at 240 min. The white arrow indicates PG45 in the cytoplasm, the green arrow indicates PG45 in the endocytic vesicles, and the blue arrow indicates PG45 escape from the endocytic vesicles, respectively, while the red bidirectional arrow denotes the cell membrane, and the yellow triangle represents the nuclear membrane.</p>
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<p>Detection of invasion rate, cell proliferation, and cytotoxicity following treatment with various agents. The horizontal axis represents the concentrations of different inhibitors, the left vertical axis represents cytotoxic effects, and the right vertical axis represents the invasion rate. Cell proliferation activity is negatively correlated with cytotoxicity. (<b>A</b>) The results obtained for EBL cells after treatment with different inhibitors, assessing invasion rate, cell proliferation, and cytotoxicity; (<b>B</b>) Similar assessments for EBTr cells; (<b>C</b>) MDBK cells; and (<b>D</b>) BT cells. The findings indicate a significant reduction in the invasion rates of all four cell types when treated with the inhibitor Chlorpromazine (CPZ) (<span class="html-italic">p</span> &lt; 0.01). Additionally, a significant decrease in the invasion rates of these cell types was observed when treated with the inhibitor Methyl-β-cyclodextrin (M-β-CD) at concentrations exceeding 4 µM (<span class="html-italic">p</span> &lt; 0.01). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; ns, no significant difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>The results of intrusion rate detection for <span class="html-italic">M. bovis</span> following siRNA knockdown of CLR. (<b>A</b>) Assessment of the interference effects of CLR-1270 siRNA oligo on clathrin (CLR) in EBL cells, revealing that the optimal interference effect is achieved at a working concentration of 20 nM. (<b>B</b>) Following the siRNA knockdown of CLR, a significant decrease (<span class="html-italic">p</span> &lt; 0.001) was observed in the intrusion rates of <span class="html-italic">M. bovis</span> strains, specifically PG45, 07801, and 08M. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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18 pages, 956 KiB  
Review
The Role of Vitamin D3 Deficiency and Colonization of the Oral Mucosa by Candida Yeast-like Fungi in the Pathomechanism of Psoriasis
by Mariola Marchlewicz, Paulina Sagan, Marta Grabowska, Magdalena Kiedrowicz, Joanna Kruk, Kamil Gill, Małgorzata Piasecka and Ewa Duchnik
J. Clin. Med. 2024, 13(22), 6874; https://doi.org/10.3390/jcm13226874 - 15 Nov 2024
Viewed by 358
Abstract
Psoriasis is a chronic inflammatory skin disease with complex pathogenesis and variable severity. Performed studies have indicated the impact of vitamin D3 deficiency on the pathogenesis of psoriasis and its severity. However, there is no clear evidence of the influence of the mucosal [...] Read more.
Psoriasis is a chronic inflammatory skin disease with complex pathogenesis and variable severity. Performed studies have indicated the impact of vitamin D3 deficiency on the pathogenesis of psoriasis and its severity. However, there is no clear evidence of the influence of the mucosal microbiome on the onset and progression of psoriasis. This review aims to present the current evidence on the role of vitamin D3 and colonization of the oral mucosa by Candida yeast-like fungi in the pathogenesis of psoriasis. Candida albicans is a common yeast that can colonize the skin and mucosal surfaces, particularly in individuals with weakened immune systems or compromised skin barriers. In psoriasis, the skin’s barrier function is disrupted, potentially making patients more susceptible to fungal infections such as Candida. Since patients with psoriasis are at increased risk of metabolic syndrome, they may experience the vicious circle effect in which chronic inflammation leads to obesity. Vitamin D3 deficiency is also associated with microbiological imbalance, which may promote excessive growth of Candida fungi. Under normal conditions, the intestinal and oral microflora support the immune system. Vitamin D3 deficiency, however, leads to disruption of this balance, which allows Candida to overgrow and develop infections. Full article
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<p>Pathogenesis of psoriasis; part I. Created with BioRender.com. When keratinocytes are exposed to trauma, they release various inflammatory mediators such as interleukin-1 (IL-1), IL-6, tumor necrosis factor α (TNFα), chemokines, and antimicrobial peptides (CAMPs), including cathelicidin (LL-37), β-defensin, and S100 proteins. In pathological conditions such as psoriasis, LL-37 or cathelicidin can bind to host DNA. This binding activates the toll-like receptor 9 (TLR-9) on dendritic cells, which then stimulates an immune response. Interferons IFNα and IFNβ secreted by keratinocytes play a role in the maturation of dendritic cells in the bone marrow and are responsible for the differentiation and proliferation of Th1 and Th17 cells, which secrete cytokines such as IL-17, IL-21, and IL-22.</p>
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<p>Pathogenesis of psoriasis; part II. Created with BioRender.com. When keratinocytes are exposed to trauma, they release various inflammatory mediators such as interleukin-1 (IL-1), IL-6, tumor necrosis factor α (TNFα), chemokines, and antimicrobial peptides (CAMPs), including cathelicidin (LL-37), β-defensin, and S100 proteins. In pathological conditions such as psoriasis, LL-37 can bind to host RNA. This binding activates the toll-like receptor 8 (TLR-8) on dendritic cells, which then stimulates migration of dendritic cells to lymph nodes and the secretion of TNFα, IL-12, and IL-23. This results in the differentiation and proliferation of Th1 and Th17 cells, which secrete cytokines such as IL-17, IL-21, and IL-22.</p>
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<p>Vitamin D metabolism. Created with BioRender.com. Vitamin D3 is produced from 7-dehydrocholesterol in the epidermis under UVB exposure, converting into cholecalciferol. Both vitamin D2 and D3 bind to DBP and are transported to the liver, where they convert to 25-hydroxyvitamin D (25(OH)D3 or calcidiol) via CYP2R1. This inactive form is then transported to the kidney’s proximal tubule, where it is hydroxylated by CYP24A1 at position 1 to form calcitriol [1,25(OH)2D3] or by CYP27B1 at position 24 to form 24,25(OH)2D3.</p>
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9 pages, 1116 KiB  
Perspective
The Immunologic Downsides Associated with the Powerful Translation of Current COVID-19 Vaccine mRNA Can Be Overcome by Mucosal Vaccines
by Maurizio Federico
Vaccines 2024, 12(11), 1281; https://doi.org/10.3390/vaccines12111281 - 14 Nov 2024
Viewed by 9572
Abstract
The action of mRNA-based vaccines requires the expression of the antigen in cells targeted by lipid nanoparticle–mRNA complexes. When the vaccine antigen is not fully retained by the producer cells, its local and systemic diffusion can have consequences depending on both the levels [...] Read more.
The action of mRNA-based vaccines requires the expression of the antigen in cells targeted by lipid nanoparticle–mRNA complexes. When the vaccine antigen is not fully retained by the producer cells, its local and systemic diffusion can have consequences depending on both the levels of antigen expression and its biological activity. A peculiarity of mRNA-based COVID-19 vaccines is the extraordinarily high amounts of the Spike antigen expressed by the target cells. In addition, vaccine Spike can be shed and bind to ACE-2 cell receptors, thereby inducing responses of pathogenetic significance including the release of soluble factors which, in turn, can dysregulate key immunologic processes. Moreover, the circulatory immune responses triggered by the vaccine Spike is quite powerful, and can lead to effective anti-Spike antibody cross-binding, as well as to the emergence of both auto- and anti-idiotype antibodies. In this paper, the immunologic downsides of the strong efficiency of the translation of the mRNA associated with COVID-19 vaccines are discussed together with the arguments supporting the idea that most of them can be avoided with the advent of next-generation, mucosal COVID-19 vaccines. Full article
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<p>Bystander effects of Spike/ACE-2 binding. Free SARS-CoV-2 Spike protein binds ACE-2-expressing cells, thereby inducing intracellular signaling, leading to the release of soluble factors. Among these, TGF-β is known to downregulate the antigen-presenting activity in APCs through MHC Class I/II downregulation. TGF-β is also a major driver of the epithelial-to-mesenchymal transition that is the basis of the development of both solid tumors and metastasis.</p>
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<p>Generation of anti-idiotype antibodies after COVID-19 vaccination. The immune system can generate antibodies against the sequences of anti-Spike antibodies recognizing the Spike domain binding the ACE-2 receptor (receptor-binding domain, RBD). Through a mechanism of molecular mimicry, these antibodies (anti-idiotype antibodies) can bind ACE-2 just like the immunogenic Spike.</p>
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14 pages, 6300 KiB  
Article
H9 Consensus Hemagglutinin Subunit Vaccine with Adjuvants Induces Robust Mucosal and Systemic Immune Responses in Mice by Intranasal Administration
by Liming Lin, Shunfan Zhu, Beibei Yang, Xin Zhang, Huimin Wu, Shixiang Wu, Li Wu, Jianhong Shu, Yulong He and Huapeng Feng
Microorganisms 2024, 12(11), 2294; https://doi.org/10.3390/microorganisms12112294 - 12 Nov 2024
Viewed by 585
Abstract
The H9N2 subtype avian influenza viruses mainly cause respiratory symptoms, reduce the egg production and fertility of poultry, and result in secondary infections, posing a great threat to the poultry industry and human health. Currently, all H9N2 avian influenza commercial vaccines are inactivated [...] Read more.
The H9N2 subtype avian influenza viruses mainly cause respiratory symptoms, reduce the egg production and fertility of poultry, and result in secondary infections, posing a great threat to the poultry industry and human health. Currently, all H9N2 avian influenza commercial vaccines are inactivated vaccines, which provide protection for immunized animals but cannot inhibit the spread of the virus and make it difficult to distinguish between the infected animals and vaccinated animals. In this study, a trimeric consensus H9 hemagglutinin (HA) subunit vaccine for the H9N2 subtype avian influenza virus based on a baculovirus expression system was first generated, and then the effects of three molecular adjuvants on the H9 HA subunit vaccine, Cholera toxin subunit B (CTB), flagellin, and granulocyte-macrophage colony-stimulating factor (GM-CSF) fused with H9 HA, and one synthetic compound, a polyinosinic–polycytidylic acid (PolyI:C) adjuvant, were evaluated in mice by intranasal administration. The results showed that these four adjuvants enhanced the immunogenicity of the H9 HA subunit vaccine for avian influenza viruses, and that GM-CSF and PolyI:C present better mucosal adjuvant activity for the H9 HA subunit vaccine. These results demonstrate that we have developed a potential universal H9 HA mucosal subunit vaccine with adjuvants in a baculovirus system that would be helpful for the prevention and control of H9N2 subtype avian influenza viruses. Full article
(This article belongs to the Topic Advances in Vaccines and Antimicrobial Therapy)
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<p>Schematic representation of the structure of HA.</p>
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<p>Immunization schedule and sample collection time points.</p>
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<p>Double-digestion identification of recombinant transfer plasmids. (<b>A</b>) pFastBac-HA, M1: DL15000 DNA marker; (<b>B</b>) pFastBac-CTB-HA; (<b>C</b>) pFastBac-FliC-HA, M2: DL10000 DNA marker; (<b>D</b>) pFastBac-GM-CSF-HA.</p>
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<p>Identification of recombinant bacmids by PCR. (<b>A</b>) rBacmid-HA, M1: DL15000 DNA marker; (<b>B</b>) rBacmid-CTB-HA, M2: DL10000 DNA marker; (<b>C</b>) rBacmid-FliC-HA; (<b>D</b>) rBacmid-GM-CSF-HA.</p>
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<p>Observation of cytopathic effect in Sf9 cells transfected with recombinant bacmids. (<b>A</b>) rBV-HA; (<b>B</b>) rBV-CTB-HA; (<b>C</b>) rBV-FliC-HA; (<b>D</b>) rBV-GM-CSF-HA; (<b>E</b>) negative control. All images were magnified at 400×.</p>
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<p>Identification of trimer formation of recombinant proteins by Western blotting. (<b>A</b>) HA; (<b>B</b>) CTB-HA; (<b>C</b>) FliC-HA; (<b>D</b>) GM-CSF-HA.</p>
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<p>Identification of HA expression with the recombinant baculoviruses by indirect immunofluorescence assay. (<b>A</b>) rBV-HA; (<b>B</b>) rBV-CTB-HA; (<b>C</b>) rBV-FliC-HA; (<b>D</b>) rBV-GM-CSF-HA; (<b>E</b>) negative control. All images were magnified at 400×.</p>
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<p>Identification of the purified proteins by SDS-PAGE. (<b>A</b>) HA; (<b>B</b>) CTB-HA; (<b>C</b>) FliC-HA; (<b>D</b>) GM-CSF-HA.</p>
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<p>Identification of the purified HA proteins by Western blotting. (<b>A</b>) HA; (<b>B</b>) CTB-HA; (<b>C</b>) FliC-HA; (<b>D</b>) GM-CSF-HA.</p>
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<p>Specific IgA and IgG antibodies were induced in NW, BALF, and sera by HA and three molecular adjuvant-fused HAs through intranasal immunization. (<b>A</b>) IgA antibody titers in NLF and BALF, (<b>B</b>) IgA antibody titers in sera, (<b>C</b>) IgG antibody titers in NLF and BALF, * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; Two-way ANOVA was used for significant analysis.</p>
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<p>Poly I:C significantly enhanced the induction of IgA and IgG antibodies by intranasal administration. (<b>A</b>) IgA antibody titers in NW and BALF; (<b>B</b>) IgA antibody titers in sera; (<b>C</b>) IgG antibody titers in sera.; **** <span class="html-italic">p</span> &lt; 0.0001; two-way ANOVA was used for significant analysis.</p>
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22 pages, 2582 KiB  
Review
Harnessing Bacillus subtilis Spore Surface Display (BSSD) Technology for Mucosal Vaccines and Drug Delivery: Innovations in Respiratory Virus Immunization
by Howra Bahrulolum, Parisa Beyranvand and Gholamreza Ahmadian
Drugs Drug Candidates 2024, 3(4), 774-795; https://doi.org/10.3390/ddc3040044 - 11 Nov 2024
Viewed by 686
Abstract
Respiratory viruses present significant global health challenges due to their rapid evolution, efficient transmission, and zoonotic potential. These viruses primarily spread through aerosols and droplets, infecting respiratory epithelial cells and causing diseases of varying severity. While traditional intramuscular vaccines are effective in reducing [...] Read more.
Respiratory viruses present significant global health challenges due to their rapid evolution, efficient transmission, and zoonotic potential. These viruses primarily spread through aerosols and droplets, infecting respiratory epithelial cells and causing diseases of varying severity. While traditional intramuscular vaccines are effective in reducing severe illness and mortality, they often fail to induce sufficient mucosal immunity, thereby limiting their capacity to prevent viral transmission. Mucosal vaccines, which specifically target the respiratory tract’s mucosal surfaces, enhance the production of secretory IgA (sIgA) antibodies, neutralize pathogens, and promote the activation of tissue-resident memory B cells (BrMs) and local T cell responses, leading to more effective pathogen clearance and reduced disease severity. Bacillus subtilis spore surface display (BSSD) technology is emerging as a promising platform for the development of mucosal vaccines. By harnessing the stability and robustness of Bacillus subtilis spores to present antigens on their surface, BSSD technology offers several advantages, including enhanced stability, cost-effectiveness, and the ability to induce strong local immune responses. Furthermore, the application of BSSD technology in drug delivery systems opens new avenues for improving patient compliance and therapeutic efficacy in treating respiratory infections by directly targeting mucosal sites. This review examines the potential of BSSD technology in advancing mucosal vaccine development and explores its applications as a versatile drug delivery platform for combating respiratory viral infections. Full article
(This article belongs to the Special Issue Fighting SARS-CoV-2 and Related Viruses)
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<p>Schematic representation highlighting the role of NALT and BALT as the first line of defense against respiratory infections. These tissues act as inductive sites for initiating immune responses to inhaled pathogens, contributing to mucosal immunity in the respiratory tract.</p>
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<p>Schematic representations of the 3D structures of <span class="html-italic">Bacillus subtilis</span> spore coat proteins B, C, and G.</p>
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<p>Schematic representation of BSSD technology for vaccine development. The recombinant vector is transformed into <span class="html-italic">Bacillus subtilis</span>, enabling antigen expression on the spore surface during sporulation. These spore-based vaccines can be administered orally or nasally, targeting mucosal immunity and offering a stable, immunogenic platform for vaccine delivery.</p>
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25 pages, 2800 KiB  
Review
Oxidative Stress in Poultry and the Therapeutic Role of Herbal Medicine in Intestinal Health
by Yuan Li, Kai Wang and Chunmei Li
Antioxidants 2024, 13(11), 1375; https://doi.org/10.3390/antiox13111375 - 10 Nov 2024
Viewed by 618
Abstract
The intensive broiler farming model has accelerated the development of the poultry farming industry. However, it has also inevitably brought about many stressors that lead to oxidative stress in the organism. The intestine is the leading site of nutrient digestion, absorption, and metabolism, [...] Read more.
The intensive broiler farming model has accelerated the development of the poultry farming industry. However, it has also inevitably brought about many stressors that lead to oxidative stress in the organism. The intestine is the leading site of nutrient digestion, absorption, and metabolism, as well as a secretory and immune organ. Oxidative stress in animal production can harm the intestine, potentially leading to significant losses for the farming industry. Under conditions of oxidative stress, many free radicals are produced in the animal’s body, attacking the intestinal mucosal tissues and destroying the barrier integrity of the intestinal tract, leading to disease. Recently, herbs have been shown to have a favorable safety profile and promising application in improving intestinal oxidative stress in poultry. Therefore, future in-depth studies on the specific mechanisms of herbs and their extracts for treating intestinal oxidative stress can provide a theoretical basis for the clinical application of herbs and new therapeutic options for intestinal oxidative stress injury during poultry farming. This review focuses on the causes and hazards of oxidative stress in the intestinal tract of poultry, and on herbs and their extracts with therapeutic potential, to provide a reference for developing and applying new antioxidants. Full article
(This article belongs to the Special Issue Oxidative Stress in Poultry Reproduction and Nutrition)
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<p>The mechanism of damage to the intestine.</p>
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16 pages, 8178 KiB  
Article
A New Probiotic Formulation Promotes Resolution of Inflammation in a Crohn’s Disease Mouse Model by Inducing Apoptosis in Mucosal Innate Immune Cells
by Carlo De Salvo, Abdullah Osme, Mahmoud Ghannoum, Fabio Cominelli and Luca Di Martino
Int. J. Mol. Sci. 2024, 25(22), 12066; https://doi.org/10.3390/ijms252212066 - 10 Nov 2024
Viewed by 479
Abstract
The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses, leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of a recently developed [...] Read more.
The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses, leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of a recently developed probiotic + amylase blend in the SAMP1/YitFc (SAMP) mouse model of CD-like ileitis. Four groups of SAMP mice were gavaged for 56 days with the following treatments: 1) probiotic strains + amylase (0.25 mg/100 µL PBS); 2) only probiotics; 3) only amylase; PBS-treated controls. Ilea were collected for GeoMx Digital Spatial Profiler (DSP) analysis and histological evaluation. Histology assessment for inflammation indicated a significantly reduced level of ileitis in mice administered the probiotics + amylase blend. DSP analysis showed decreased abundance of neutrophils and increased abundance of dendritic cells, regulatory T cells, and macrophages, with a significant enrichment of five intracellular pathways related to apoptosis, in probiotics + amylase-treated mice. Increased apoptosis occurrence was confirmed by (TdT)- deoxyuridine triphosphate (dUTP)-biotin nick end labeling assay. Our data demonstrate a beneficial role of the probiotic and amylase blend, highlighting an increased apoptosis of innate immunity-associated cell subsets, thus promoting the resolution of inflammation. Hence, we suggest that the developed probiotic enzyme blend may be a therapeutic tool to manage CD and therefore is a candidate formulation to be tested in clinical trials. Full article
(This article belongs to the Special Issue The Role of Microbiota in Immunity and Inflammation)
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Graphical abstract

Graphical abstract
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<p>Amylase and probiotics are both necessary to ameliorate inflammation in SAMP mice. (<b>A</b>) Histology evaluation shows significant attenuation of ileitis in mice treated with amylase plus probiotic mix (P + A) in comparison with the mice treated with only amylase (A), only probiotic mix (P), or PBS (control) (C) (one-way ANOVA, 8.50 ± 2.46 vs. 13.20 ± 2.25 vs. 13.30 ± 4.08 vs. 15.20 ± 2.97; <span class="html-italic">p</span> &lt; 0.001). (<b>B</b>) Representative pictures of H&amp;E-stained sections indicate that mice treated with both amylase and probiotic mix have better-preserved architecture of the villi (red arrows) and less presence of inflammatory cells in the mucosal and submucosal layer (yellow arrows) compared to the other three groups. Data are represented as mean ± SEM and are representative of two independent experiments; *** <span class="html-italic">p</span> &lt; 0.001. N = 10/group. 10X + 1.25 original mag.</p>
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<p>Digital spatial profiling of genetic expression performed on mucosal ileal tissue in SAMP mice. (<b>A</b>) Schematic workflow showing immunohistochemistry (IHC)/immunofluorescence (IF) staining of slides embedded in paraffin with markers for PanCK, CD45, and nuclei in the ROIs. (<b>B</b>) Representative images of mucosal layers of stained tissues with segments superimposed and with probe counts indicating PanCK<sup>+</sup> cells (Cy3568 nm; yellow), CD45<sup>+</sup> cells (Texas red, 615 nm; red), and nuclear staining (FITC, 525 nm; green). N = 6 mice/group; N = 6 ROIs/mouse.</p>
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<p>Probiotics mix coupled with amylase exerts an enhanced immunomodulatory effect in the mucosal layer. (<b>A</b>) Principal component analysis showing the variance among ROIs based on cell type abundance beta estimates (PC1 variance: 22.3%; PC2 variance: 4.3%), highlighting significant difference in the mice administered the probiotics + amylase mix (P + A) compared to only amylase (A), only probiotics (P), and control (C) groups (PC1: Kruskal–Wallis test, 69.94 ± 3.94 vs. 5.39 ± 7.35 vs. −42.63 ± 5.89 vs. −30.00 ± 5.56; <span class="html-italic">p</span> &lt; 0.001; N = 36 ROIs/group). (<b>B</b>) Dendrogram showing estimated relative abundance of immune cell subsets in microenvironment segments, indicating a significant decrease in (<b>C</b>) neutrophils (one-way ANOVA, 1.22 × 10<sup>−3</sup> ± 0.14 × 10<sup>−3</sup> vs. 6.43 × 10<sup>−3</sup> ± 1.35 × 10<sup>−3</sup> vs. 5.30 × 10<sup>−3</sup> ± 0.61 × 10<sup>−3</sup> vs. 4.21 × 10<sup>−3</sup> ± 0.76 × 10<sup>−3</sup>; <span class="html-italic">p</span> &lt; 0.02) and increase in (<b>D</b>) dendritic cells (one-way ANOVA, 2.29 × 10<sup>−2</sup> ± 0.46 × 10<sup>−2</sup> vs. 1.12 × 10<sup>−2</sup> ± 0.33 × 10<sup>−2</sup> vs. 0.91 × 10<sup>−2</sup> ± 0.25 × 10<sup>−2</sup>; <span class="html-italic">p</span> &lt; 0.02), (<b>E</b>) innate lymphoid cells (ILC)s (one-way ANOVA, 7.19 × 10<sup>−2</sup> ± 0.42 × 10<sup>−2</sup> vs. 4.94 × 10<sup>−3</sup> ± 0.63 × 10<sup>−2</sup> vs. 5.35 × 10<sup>−2</sup> ± 0.36 × 10<sup>−2</sup> vs. 4.68 × 10<sup>−2</sup> ± 0.43 × 10<sup>−2</sup>; <span class="html-italic">p</span> &lt; 0.02), (<b>F</b>) macrophages (one-way ANOVA, 6.15 × 10<sup>−2</sup> ± 1.29 × 10<sup>−2</sup> vs. 2.28 × 10<sup>−2</sup> ± 0.58 × 10<sup>−2</sup> vs. 3.73 × 10<sup>−2</sup> ± 0.34 × 10<sup>−2</sup> vs. 2.21 × 10<sup>−2</sup> ± 0.46 × 10<sup>−2</sup>; <span class="html-italic">p</span> &lt; 0.02), and (<b>G</b>) regulatory T cells (Treg)s (one-way ANOVA, 1.05 × 10<sup>−2</sup> ± 0.12 × 10<sup>−2</sup> vs. 0.80 × 10<sup>−2</sup> ± 0.07 × 10<sup>−2</sup> vs. 0.44 × 10<sup>−2</sup> ± 0.08 × 10<sup>−2</sup> vs. 0.30 × 10<sup>−2</sup> ± 0.12 × 10<sup>−2</sup>; <span class="html-italic">p</span> &lt; 0.02) in the group treated with probiotic + amylase compared with control, only amylase, and only probiotics groups. Data are represented as mean ± SEM and are representative of two independent experiments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.02; *** <span class="html-italic">p</span> &lt; 0.001. N = 6 mice/group; N = 6 ROIs/mouse.</p>
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<p>Probiotic + amylase blend stimulates apoptosis in immune cell subsets. (<b>A</b>–<b>C</b>) Volcano plots obtained from the pathway analysis of CD45<sup>+</sup> cells in ROIs of the four experimental groups. Statistically significant different apoptosis-related pathways are labeled in yellow (<span class="html-italic">p</span> &lt; 0.05; normalized enrichment score &gt; ±2) and in gray when not significantly different. Amylase and the probiotic mix are both necessary to significantly induce apoptosis in CD45<sup>+</sup> cells. Comparison between probiotics without amylase and the control group shows no significantly different apoptosis-related pathways between the two groups. (<b>D</b>) Heatmap of differentially expressed genes related to apoptosis pathways between the control group and probiotic + amylase, (<b>E</b>) only probiotics, and (<b>F</b>) only amylase groups. (<b>G</b>) Representative IF photomicrographs of TUNEL assay show the increased number of apoptotic TUNEL-positive cells (green, Alexa Fluor™ 488) and fewer neutrophils (red, Alexa Fluor™ 594) deep in the lamina propria of mice treated with the probiotic + amylase blend compared to the control group (DAPI, blue). 40X original mag. N = 6 mice/group.</p>
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19 pages, 10194 KiB  
Article
Development of High-Production Bacterial Biomimetic Vesicles for Inducing Mucosal Immunity Against Avian Pathogenic Escherichia coli
by Yue Li, Yuji Quan, Peng Chen, Xiangkai Zhuge, Tao Qin, Sujuan Chen, Daxin Peng and Xiufan Liu
Int. J. Mol. Sci. 2024, 25(22), 12055; https://doi.org/10.3390/ijms252212055 - 9 Nov 2024
Viewed by 524
Abstract
To evaluate the immunoprotective effect of bacterial biomimetic vesicles (BBVs) against avian pathogenic Escherichia coli (APEC), a ΔtolA J11 mutant strain was generated by deleting the tolA gene in the low pathogenic O78 serotype J11 strain. The total protein content of outer [...] Read more.
To evaluate the immunoprotective effect of bacterial biomimetic vesicles (BBVs) against avian pathogenic Escherichia coli (APEC), a ΔtolA J11 mutant strain was generated by deleting the tolA gene in the low pathogenic O78 serotype J11 strain. The total protein content of outer membrane vesicles (OMVs) derived from the ΔtolA J11 strain exhibited a sevenfold increase compared to the wild-type strain. Additionally, high-pressure homogenization technology was employed to produce BBVs, resulting in a sixfold increase in total protein content compared to spontaneously secreted OMVs from ΔtolA J11. The immunogenicity of both OMVs and BBVs was assessed through intranasal or intramuscular immunization in specific pathogen-free (SPF) chickens. Results demonstrated that intranasal immunization with OMVs or BBVs in chickens elicited specific IgY antibodies against APEC outer membrane proteins and specific sIgA antibodies in the nasal cavity and trachea, as well as a significant increase in the proliferation response of chicken peripheral blood lymphocytes. The bacterial load in the blood and various organs of the challenged chickens were significantly reduced, resulting in a 66.67% and 58.30% survival rate against a high pathogenic serotype O78 strain challenge, while the control group exhibited only a 16.67% survival rate. The intramuscular immunization with OMVs or BBVs in chickens only induced specific IgY antibodies, with a survival rate of only 33.33% for challenged chickens during the same period. Therefore, intranasal vaccination of the highly productive BBVs is capable of eliciting an immune response similar to that of OMVs and providing protection against APEC infection, thus offering innovative insights for the advancement of APEC vaccines. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>The <span class="html-italic">tolA</span> gene mutation in <span class="html-italic">E. coli</span> J11 strain increases the total protein content of its released OMVs. (<b>A</b>) A schematic diagram of <span class="html-italic">tolA</span> gene deletion strain constructed by the λ-Red homologous recombination method; (<b>B</b>) the <span class="html-italic">tolA</span> mutant was verified by PCR amplification with the size of the <span class="html-italic">tolA</span> gene being 1266 bp; (<b>C</b>) the growth curve of the mutant and wild-type strains; (<b>D</b>) morphological characteristics of vesicles observed by transmission electron microscopy (red arrow); (<b>E</b>) particle sizes of vesicles; (<b>F</b>) determination of the total protein amount by the BCA method; (<b>G</b>) SDS-PAGE analysis (0.5 μg/well). Data were representative of three independent experiments and displayed the mean ± SD. ns: no significance, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Production of BBV by high-pressure homogenization technology. (<b>A</b>) Schematic diagram of bacterial membrane vesicle production; (<b>B</b>) electron microscopy analysis of OMVs and BBVs; (<b>C</b>) particle size analysis of OMVs and BBVs; (<b>D</b>) determination of total protein concentration by BCA assay; (<b>E</b>) SDS-PAGE analysis (0.5 μg/well); (<b>F</b>): DNA content in samples of Δ<span class="html-italic">tolA</span> J11 whole cells (WC), BBV, and OMV were analyzed by agarose gel electrophoresis. Data are representative of three independent experiments and displayed as mean ± SD. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>HD11 cells recognize and internalize OMVs and BBVs. (<b>A</b>) Confocal microscope observation: OMVs and BBVs were stained with DiD (red), and nuclei were stained with DAPI (blue). mRNA expression of IL-1β (<b>B</b>), IL-6 (<b>C</b>), TNF-α (<b>D</b>), MHC-IIβ (<b>E</b>), and iNOS (<b>F</b>) in HD11 cells stimulated by OMVs and BBVs. Data were representative of three independent experiments and displayed mean ± SD. ns: no significance, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Antibody response induced by intranasal (IN) or intramuscular (IM) immunization with OMVs or BBVs in chickens. (<b>A</b>) Schematic of chicken immunization experiments. (<b>B</b>) Body weight gain curves of chickens after immunization. C&amp;D: Omps-specific sIgA antibodies in nasal cavity (<b>C</b>) and trachea (<b>D</b>) of immunized chickens detected by enzyme-linked immunosorbent assay (<span class="html-italic">n</span> = 3). (<b>E</b>) Omps-specific IgY antibodies in serum samples of immunized chickens detected by enzyme-linked immunosorbent assay (<span class="html-italic">n</span> = 5). The 1st, 2nd, and 3rd referred to samples were collected at the first, second, and third post-immunizations, respectively. The data are represented as the mean ± SD. Difference lowercase letters denote statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Cellular immune response induced by intranasal (IN) or intramuscular (IM) immunization with OMVs or BBVs in chickens. Representative flow cytometry plots (<b>A</b>) and corresponding statistical analysis (<b>B</b>–<b>D</b>) illustrating proportions of CD4<sup>+</sup> and CD8<sup>+</sup> T lymphocytes in peripheral blood lymphocytes of immunized chickens. Additionally, mRNA expression levels of IFN−γ (<b>E</b>), IL−4 (<b>F</b>), and IL−17A (<b>G</b>) in the PBMC were assessed. Proliferation response of chicken peripheral blood lymphocytes was evaluated using CCK−8 method. Compared to PBS, ns: no significance, ** <span class="html-italic">p</span> &lt; 0.01 (<b>H</b>). Data represented as mean ± SD. Difference lowercase letters denoted statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Immunoprotective effects induced by intranasal (IN) or intramuscular (IM) immunization with OMVs or BBVs in chickens. (<b>A</b>) Survival rates of immunized chickens following challenge with lethal dose of APEC serotype O78 (<span class="html-italic">n</span> = 10). (<b>B</b>) Bacterial distribution in various tissues and organs at 24 h post-APEC challenge. (<b>C</b>) Bacterial loads in blood at 24 h and 96 h post-APEC challenge. (<b>D</b>–<b>F</b>) Serum concentrations of IL-1β, IL-6, and TNF-α at 24 h post-APEC challenge. Data represented as mean ± SD. Difference lowercase letters denoted statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Histopathological lesion in chickens following intranasal (IN) or intramuscular (IM) immunization with OMVs or BBVs and challenge. A&amp;B: Hearts (<b>A</b>) and lungs (<b>B</b>) were collected from chickens at 3 days post-infection (dpi) and subjected to histological analysis after staining with hematoxylin and eosin (H&amp;E); (<b>C</b>): Pathological damage scoring (<span class="html-italic">n</span> = 3). Pathological damage was scored on a scale of 0 to 3 (0, not present; 1, slight; 2, moderate; 3, severe). Data represented as mean ± SD. Difference lowercase letters denoted statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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