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Search Results (845)

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21 pages, 3591 KiB  
Article
Effects of Vegetable and Fruit Juicing on Gut and Oral Microbiome Composition
by Maria Luisa Savo Sardaro, Veronika Grote, Jennifer Baik, Marco Atallah, Katherine Ryan Amato and Melinda Ring
Nutrients 2025, 17(3), 458; https://doi.org/10.3390/nu17030458 - 27 Jan 2025
Viewed by 142
Abstract
Background: In recent years, juicing has often been promoted as a convenient way to increase fruit and vegetable intake, with juice-only diets marketed for digestive cleansing and overall health improvement. However, juicing removes most insoluble fiber, which may diminish the health benefits of [...] Read more.
Background: In recent years, juicing has often been promoted as a convenient way to increase fruit and vegetable intake, with juice-only diets marketed for digestive cleansing and overall health improvement. However, juicing removes most insoluble fiber, which may diminish the health benefits of whole fruits and vegetables. Lower fiber intake can alter the microbiota, affecting metabolism, immunity, and mental health, though little is known about juicing’s specific effects on the microbiota. This study addresses this gap by exploring how juicing impacts gut and oral microbiome composition in an intervention study. Methods: Fourteen participants followed one of three diets—exclusive juice, juice plus food, or plant-based food—for three days. Microbiota samples (stool, saliva, and inner cheek swabs) were collected at baseline, after a pre-intervention elimination diet, immediately after juice intervention, and 14 days after intervention. Moreover, 16S rRNA gene amplicon sequencing was used to analyze microbiota taxonomic composition. Results: The saliva microbiome differed significantly in response to the elimination diet (unweighted UniFrac: F = 1.72, R = 0.06, p < 0.005; weighted UniFrac: F = 7.62, R = 0.23, p-value = 0.0025) with a significant reduction in Firmicutes (p = 0.004) and a significant increase in Proteobacteria (p = 0.005). The juice intervention diets were also associated with changes in the saliva and cheek microbiota, particularly in the relative abundances of pro-inflammatory bacterial families, potentially due to the high sugar and low fiber intake of the juice-related products. Although no significant shifts in overall gut microbiota composition were observed, with either the elimination diet or the juice intervention diets, bacterial taxa associated with gut permeability, inflammation, and cognitive decline increased in relative abundance. Conclusions: These findings suggest that short-term juice consumption may negatively affect the microbiota. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>Non-metric multidimensional scaling (NMDS) plots of unweighted UniFrac (<b>A</b>) and weighted UniFrac distances (<b>B</b>) for cheek samples at baseline and pre-intervention time points, in response to the elimination diet; MDS: metric multidimensional scaling.</p>
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<p>Non-metric multidimensional scaling (NMDS) plots of unweighted UniFrac (<b>A</b>) and weighted UniFrac distances (<b>B</b>) for saliva samples at baseline and pre-intervention time points, in response to the elimination diet; MDS: metric multidimensional scaling.</p>
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<p>Relative abundances of bacterial phyla in cheek samples (<b>A</b>) and saliva samples (<b>B</b>) at baseline at pre-intervention time points in response to the elimination diet.</p>
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<p>Relative abundance of bacterial phyla in fecal samples in response to the elimination diet at baseline and pre-intervention time points.</p>
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<p>Relative abundance heatmap of genus and bacterial species in fecal samples in response to elimination diet, the baseline and pre-intervention time pointsare reported as log10 of percent abundance.</p>
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<p>Non-metric multidimensional scaling (NMDS) plots with the unweighted Unifrac (<b>A</b>) and weighted Unifrac distances (<b>B</b>) of saliva samples at pre-intervention, post-intervention, and 14-day post-intervention time points for the three diet types.</p>
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<p>Relative abundance of bacterial phyla in cheek samples at pre-intervention, post-intervention, 14 days post intervention for the three diet types.</p>
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<p>Relative abundance heatmap on the centered log-ratio (CLR) transformed values of bacterial families with prevalence higher than 20% in cheek samples in the three diet types at pre-intervention (2-pre-intervention), post-intervention (3-Food_Plant_based, 3-Food+Juice, 3-Juice), 14 days post intervention (4-Food_Plant_based, 4-Food+Juice, 4-Juice) time points.</p>
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<p>Relative abundance of bacterial phyla in saliva samples at pre-intervention, post-intervention, 14 days post intervention for the three diet types.</p>
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<p>Relative abundance heatmap on the centered log-ratio (CLR) transformed values of bacterial families with prevalence higher than 20% in saliva samples in the three diet types at pre-intervention (2-pre-intervention), post-intervention (3-Food_Plant_based, 3-Food+Juice, 3-Juice), 14 days post intervention (4-Food_Plant_based, 4-Food+Juice, 4-Juice) time points.</p>
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<p>Relative abundance of bacterial phyla in fecal samples at pre-intervention, post-intervention, 14 days post intervention for the three diet types.</p>
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<p>Heatmap based on the centered log-ratio (CLR) transformed values of bacterial families with prevalence higher than 20% in fecal samples in the three diets intervention at pre-intervention (2-pre-intervention), post-intervention (3-Food_Plant_based, 3-Food+Juice, 3-Juice), 14 days post intervention (4-Food_Plant_based, 4-Food+Juice, 4-Juice) time points.</p>
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21 pages, 1195 KiB  
Article
Exploring Competitive Relationship Between Haemophilus parainfluenzae and Mitis Streptococci via Co-Culture-Based Molecular Diagnosis and Metabolomic Assay
by Yeseul Choi, Jinuk Jeong, Youngjong Han, Miyang Han, Byungsun Yu and Kyudong Han
Microorganisms 2025, 13(2), 279; https://doi.org/10.3390/microorganisms13020279 - 26 Jan 2025
Viewed by 229
Abstract
Various bacterial strains with nitrate-reducing capacity (NRC), such as Haemophilus, Actinomyces, and Neisseria, are known to promote NH3 production, control pH in the oral cavity, and inhibit the growth of aciduric bacteria. However, experimental evidence on various estimated bacterial [...] Read more.
Various bacterial strains with nitrate-reducing capacity (NRC), such as Haemophilus, Actinomyces, and Neisseria, are known to promote NH3 production, control pH in the oral cavity, and inhibit the growth of aciduric bacteria. However, experimental evidence on various estimated bacterial networks within the salivary microbiome is insufficient. This study aims to explore potential bacterial compositional competition observed within saliva samples from dental caries patients through a co-culture assay of mitis Streptococci, which is a primary colonizer in the salivary microbiome, and nitrate-reducing bacteria Haemophilus parainfluenzae. We investigated bacterial growth efficiency change by co-culture time using the qRT-PCR method. In addition, we applied LC/Q-TOF-based metabolites screening to confirm metabolic interactions between oral bacterial species and their association with dental caries from a metabolomics perspective. As a result, we first found that the nitrate reduction ability of H. parainfluenzae is maintained even in a co-culture environment with the mitis Streptococci group through a nitrate reduction test. However, nitrate reduction efficiency was hindered when compared with monoculture-based nitrate reduction test results. Next, we designed species-specific primers, and we confirmed by qRT-PCR that there is an obvious competitive relationship in growth efficiency between H. parainfluenzae and two mitis Streptococci (S. australis and S. sanguinis). Furthermore, although direct effects of nitrate reduction on competition have not been identified, we have potentially confirmed through LC/Q-TOF-based metabolite screening analysis that the interaction of various metabolic compounds synthesized from mitis Streptococci is driving inter-strain competition. In particular, we constructed a basic reference core-metabolites list to understand the metabolic network between each target bacterial species (H. parainfluenzae and mitis Streptococci) within the salivary microbiome, which still lacks accumulated research data. Ultimately, we suggest that our data have potential value to be referenced in further metagenomics and metabolomics-based studies related to oral health care. Full article
(This article belongs to the Section Public Health Microbiology)
19 pages, 6419 KiB  
Article
Efficacy of Tocopherol vs. Chlorhexidine in the Management of Oral Biopsy Site: A Randomized Clinical Trial
by Arianna Baldin, Clara Nucibella, Claudia Manera and Christian Bacci
J. Clin. Med. 2025, 14(3), 788; https://doi.org/10.3390/jcm14030788 (registering DOI) - 25 Jan 2025
Viewed by 252
Abstract
Background/Objectives: Chlorhexidine digluconate (CHX) is widely regarded as the gold standard for oral mucosa antiseptic treatments but has been associated with delayed healing, scar formation, microbiome alterations, and fibroblast toxicity. Tocopherol, with its ability to accelerate tissue healing and minimal side effects, [...] Read more.
Background/Objectives: Chlorhexidine digluconate (CHX) is widely regarded as the gold standard for oral mucosa antiseptic treatments but has been associated with delayed healing, scar formation, microbiome alterations, and fibroblast toxicity. Tocopherol, with its ability to accelerate tissue healing and minimal side effects, has emerged as a potential alternative. This randomized clinical trial aimed to compare the efficacy of topical tocopherol acetate and 0.2% chlorhexidine in managing postoperative pain and wound healing following oral cavity biopsies. Methods: Seventy-seven patients undergoing oral biopsies were divided into two groups: the test group (tocopherol acetate) and the control group (0.2% chlorhexidine). Pain was assessed using VAS (Visual Analogue Scale) scores on days 1 and 6 postoperatively, and wound healing was evaluated through measurements of the biopsy site’s height and width from standardized photographs analyzed with ImageJ. Painkiller use was also documented. The study followed CONSORT (Consolidated Standards of Reporting Trials) guidelines, with ethical approval from the Padua Ethics Committee and registration on ISRCTN. Results: No significant differences were found between the groups in VAS scores, wound dimensions, or painkiller use (p > 0.05). However, significant pain reduction within each group was observed (p < 0.0001). Conclusions: Tocopherol acetate showed comparable efficacy to chlorhexidine, suggesting it could be a viable alternative for postoperative care in oral surgery. Full article
(This article belongs to the Special Issue Current Challenges in Oral Surgery)
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<p>Participant flowchart according to CONSORT diagram [<a href="#B38-jcm-14-00788" class="html-bibr">38</a>]. ΔH and ΔW: Changes from day 1 to day 6 in wound dimensions were compared between the two groups (tocopherol acetate vs. chlorhexidine digluconate) with the Mann–Whitney test, and the effect was calculated as the difference of medians with 95% bootstrap confidence interval.</p>
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<p>Box plot of the change in VAS from day 1 to day 6 in the two groups.</p>
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<p>Box plot showing the change in H and W in the test group and in the control group.</p>
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<p>Box plot showing the initial and final values of H and W in test group and control group.</p>
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<p>A biopsy in the loose mucosa (buccal mucosa) in the immediately postop and at day 6.</p>
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<p>A biopsy in the attached mucosa (palate) in the immediately postop and at day 6.</p>
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<p>ImageJ splash screen when opening the program.</p>
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<p>With the “straight” instrument, the fifth starting from the right, the measurement of 1 mm is drawn on the periodontal probe.</p>
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<p>Using “analyze” &gt; “set scale” you configure the known length.</p>
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<p>In the window that appears, enter the “known distance” and the “unit of length”, then 1 mm.</p>
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<p>With the “straight” instrument you go to trace the measurement on the lesion, and through “analyze” &gt; “measure” you obtain the desired measurement in millimeters.</p>
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<p>Through the window that appears, the measurement taken in the set unit of measurement is displayed in “length”.</p>
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21 pages, 6174 KiB  
Article
Probiotics as Renal Guardians: Modulating Gut Microbiota to Combat Diabetes-Induced Kidney Damage
by Saleh Bakheet Al-Ghamdi
Biology 2025, 14(2), 122; https://doi.org/10.3390/biology14020122 - 24 Jan 2025
Viewed by 425
Abstract
Gut microbiota plays a pivotal role in various health challenges, particularly in mitigating diabetes-induced renal damage. Numerous studies have highlighted that modifying gut microbiota is a promising therapeutic strategy for preserving kidney function and mitigating diabetes-related complications. This study aimed to evaluate the [...] Read more.
Gut microbiota plays a pivotal role in various health challenges, particularly in mitigating diabetes-induced renal damage. Numerous studies have highlighted that modifying gut microbiota is a promising therapeutic strategy for preserving kidney function and mitigating diabetes-related complications. This study aimed to evaluate the protective effects of Lactobacillus acidophilus ATCC 4356 supplementations on kidney health in a rat model of diabetes-induced renal damage. Four groups were studied: control, probiotic supplementation, diabetic, and diabetic with probiotic supplementation. Diabetes was induced using a single streptozotocin (STZ) injection after a 12 h fast, and probiotic supplementation (1 × 10⁹ CFU/kg daily) was administered two weeks prior to diabetes induction and continued throughout the experimental period. Weekly assessments included fasting blood glucose, insulin, glycation markers, and kidney function tests. Glucose metabolism and insulin sensitivity were analyzed through oral glucose tolerance test (OGTT) and insulin sensitivity test (IST). The microbiome was analyzed using 16S rRNA gene sequencing to evaluate changes in diversity and composition. Probiotic supplementation significantly enhanced microbial diversity and composition. Alpha diversity indices such as Shannon and Chao1 demonstrated higher values in the probiotic-treated diabetic group compared to untreated diabetic rats. The Firmicutes/Bacteroidetes ratio, a key indicator of gut health, was also restored in the probiotic-treated diabetic group. Results: Probiotic supplementation significantly improved glycemic control, reduced fasting blood glucose levels, and enhanced insulin sensitivity in diabetic rats. Antioxidant enzyme levels, depleted in untreated diabetic rats, were restored, reflecting reduced oxidative stress. Histological analysis showed better kidney structure, reduced inflammation, and decreased fibrosis. Furthermore, the Comet assay results confirmed a reduction in DNA damage in probiotic-treated diabetic rats. Conclusion: Lactobacillus acidophilus ATCC 4356 supplementation demonstrated significant protective effects against diabetes-induced renal damage by restoring gut microbiota diversity, improving glycemic control, and reducing oxidative stress. These findings highlight the potential of targeting the gut microbiota and its systemic effects on kidney health as a therapeutic approach for managing diabetes-related complications. Further research is needed to optimize probiotic treatments and assess their long-term benefits in diabetes management and kidney health. Full article
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<p>(<b>A</b>,<b>B</b>) The impact of <span class="html-italic">Lactobacillus acidophilus</span> on body weight and insulin levels in diabetic rats. <span class="html-italic">* p</span> &lt; 0.05.</p>
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<p>(<b>A</b>–<b>F</b>) The influence of <span class="html-italic">Lactobacillus acidophilus</span> on glucose tolerance and insulin sensitivity in T2D rats. <span class="html-italic">* p</span> &lt; 0.05.</p>
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<p>(<b>A</b>–<b>G</b>) Impact of <span class="html-italic">Lactobacillus acidophilus</span> on kidney function and antioxidant markers in T2D rats. <span class="html-italic">* p</span> &lt; 0.05.</p>
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<p>(Image (control—LA—T2D—T2D+LA) (<b>A</b>–<b>C</b>)) This figure shows how <span class="html-italic">Lactobacillus acidophilus</span> supplementation protects kidneys against tissue damage in type 2 diabetes mellitus (T2DM) rats. Control (C), <span class="html-italic">Lactobacillus acidophilus</span>, T2DM treated (T2D), and T2DM treated with <span class="html-italic">Lactobacillus acidophilus</span> (T2D+LA) are among the groups shown. Comparative to the normal control group, statistical significance is indicated as <span class="html-italic">p</span> &lt; 0.05. Furthermore, mentioned against the diabetic control group are * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. Image 1 (Control) in Image (Control–T2D+LA) microscopic images derived from hematoxylin and eosin (H&amp;E)-stained kidney sections. The kidney tissue looks normal. There is no evidence of damage or inflammation among the well-defined and orderly glomeruli. This picture stands for the control or healthy condition. Image 2: (C+LA) This picture resembles the first one (control) rather exactly. The glomeruli or surrounding tissue are not clearly changed or damaged. One could regard it as either normal or almost normal. Third image (T2D): This picture clearly exhibits pathogenic changes. An accumulation of inflammatory cells in the circular area points to the fibrosis or inflammation there. Furthermore, changing in the surrounding tissue are indications of more severe kidney damage than in the first two pictures. Fourth image (T2D+LA): Less dramatic changes are shown in this picture than in Image 3. Though there are still some indications of enlarged intercellular spaces and mild degenerative changes, the glomeruli seem in better condition with better tissue organization. Though not totally normal, this condition is better than in Image 3. Having n = 10 for every group, the data are shown as the median interquartile range (IQR). These results highlight, by lowering structural damage in diabetic kidney tissues, the possible kidney-protective action of <span class="html-italic">Lactobacillus acidophilus</span>.</p>
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<p>Four separate groups of rats—the control group, the type 2 diabetes (T2D) group, and the type 2 diabetes with <span class="html-italic">Lactobacillus acidophilus</span> treatment (T2D+LA) group—have their tail lengths (µm) shown here. The experimental technique used the overnight alkaline comet assay, which is good in identifying DNA single-stranded breaks, double-stranded breaks, and alkali-labile sites.</p>
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<p>Bacterial phyla proportions.</p>
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<p>Shannon diversity index.</p>
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<p>Firmicutes/Bacteroidetes ratio.</p>
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<p>Bacterial abundance heatmap.</p>
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<p>Microbial characteristics.</p>
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<p><span class="html-italic">Firmicutes</span>/<span class="html-italic">Bacteroidetes</span> proportions.</p>
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<p>PCoA of microbial communities.</p>
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21 pages, 700 KiB  
Review
Cariogenic Microbiota and Emerging Antibacterial Materials to Combat Dental Caries: A Literature Review
by Jingwei Cao, Qizhao Ma, Jia Shi, Xinyue Wang, Dingwei Ye, Jingou Liang and Jing Zou
Pathogens 2025, 14(2), 111; https://doi.org/10.3390/pathogens14020111 - 23 Jan 2025
Viewed by 580
Abstract
Dental caries is the most common oral disease in the world and a chronic infectious disease. The cariogenic microbiome plays an important role in the process of caries. The ecological imbalance of microbiota leads to low pH, which causes caries. Therefore, antibacterial materials [...] Read more.
Dental caries is the most common oral disease in the world and a chronic infectious disease. The cariogenic microbiome plays an important role in the process of caries. The ecological imbalance of microbiota leads to low pH, which causes caries. Therefore, antibacterial materials have always been a hot topic. Traditional antibacterial materials such as cationic antibacterial agents, metal ion antibacterial agents, and some natural extract antibacterial agents have good antibacterial effects. However, they can cause bacterial resistance and have poor biological safety when used for long-term purposes. Intelligent antibacterial materials, such as pH-responsive materials, nanozymes, photoresponsive materials, piezoelectric materials, and living materials are emerging antibacterial nano-strategies that can respond to the caries microenvironment or other specific stimuli to exert antibacterial effects. Compared with traditional antibacterial materials, these materials are less prone to bacterial resistanceand have good biological safety. This review summarizes the characteristics of cariogenic microbiota and some traditional or emerging antibacterial materials. These emerging antibacterial materials can accurately act on the caries microenvironment, showing intelligent antibacterial effects and providing new ideas for caries management. Full article
(This article belongs to the Special Issue Oral Microbes and Oral Diseases)
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<p>Schematic diagram of emerging antibacterial materials for dental caries.</p>
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2 pages, 136 KiB  
Abstract
Breastfeeding Characteristics Are Associated with Minor Changes in the Human Milk Microbiome
by Ruomei Xu, Mark P. Nicol, Ali S. Cheema, Jacki L. McEachran, Sharon L. Perrella, Zoya Gridneva, Donna T. Geddes and Lisa F. Stinson
Proceedings 2025, 112(1), 20; https://doi.org/10.3390/proceedings2025112020 - 21 Jan 2025
Viewed by 312
Abstract
Human milk has a microbiome that contains a wide variety of typical oral and skin bacteria, suggesting that the bacterial communities in the infant oral cavity and maternal skin contribute to the development of the human milk microbiome [...] Full article
19 pages, 3086 KiB  
Article
Relationship Between the Salivary Microbiome and Oral Malodor Metabolites in Older Thai Individuals with Periodontitis and the Cytotoxic Effects of Malodor Compounds on Human Oral Squamous Carcinoma (HSC-4) Cells
by Witsanu Srila, Kritsana Sripilai, Thunwa Binlateh, Peungchaleoy Thammanichanon, Watcharaphol Tiskratok, Parinya Noisa and Paiboon Jitprasertwong
Dent. J. 2025, 13(1), 36; https://doi.org/10.3390/dj13010036 - 16 Jan 2025
Viewed by 479
Abstract
Background/Objectives: Halitosis is primarily caused by the activity of oral microorganisms. In this study, we employed metagenomic sequencing and metabolomic approaches to investigate the differences in salivary microbiota and metabolite profiles between individuals with halitosis and periodontitis and healthy controls. Additionally, we [...] Read more.
Background/Objectives: Halitosis is primarily caused by the activity of oral microorganisms. In this study, we employed metagenomic sequencing and metabolomic approaches to investigate the differences in salivary microbiota and metabolite profiles between individuals with halitosis and periodontitis and healthy controls. Additionally, we expanded the study to examine how oral malodorous compounds interact with human oral squamous carcinoma (HSC-4) cells. Methods: Saliva samples were collected and analyzed using Ultra-High Performance Liquid Chromatography–Mass Spectrometry (UHPLC-MS) to identify metabolites. We then assessed the correlations between the microbiota and metabolites. Furthermore, the impact of oral malodorous substances on HSC-4 cells was investigated by evaluating apoptosis, antioxidant activity, and inflammatory properties. Results: The microbiota and metabolite profiles showed significant differences between the halitosis with periodontitis group and the periodontally healthy group. The halitosis with periodontitis group exhibited significantly higher relative abundances of eight genera: Tannerella, Selenomonas, Bacteroides, Filifactor, Phocaeicola, Fretibacterium, Eubacterium saphenum, and Desulfobulbus. In contrast, the periodontally healthy group showed significantly higher relative abundances of Family XIII UCG-001, Haemophilus, and Streptobacillus. Two metabolites, 2,3-dihydro-1H-indole and 10,11-dihydro-12R-hydroxy-leukotriene E4, were significantly higher in individuals with halitosis and periodontitis. In the treatment of HSC-4 cells with metabolites, dimethyl sulfide (DMS) did not show significant effects while indole appeared to induce cell death in HSC-4 cells by triggering apoptotic pathways. Additionally, both indole and DMS affected the inflammatory and antioxidant properties of HSC-4 cells. Conclusions: This study provides insights into the mechanisms of halitosis by exploring the correlations between microbiota and metabolite profiles. Furthermore, oral metabolites were shown to impact the cellular response of HSC-4 cells. Full article
(This article belongs to the Section Oral Hygiene, Periodontology and Peri-implant Diseases)
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<p>Comparison of the microbial richness and diversity of saliva samples from the halitosis with periodontitis and the periodontally healthy groups. (<b>a</b>) The Venn diagram illustrates the number of amplicon sequence variants (ASVs) that are shared and distinct between the groups, providing insight into the similarity and overlap of the ASVs between the groups. (<b>b</b>) Comparison of the alpha diversity indices between the halitosis with periodontitis and periodontally healthy groups. (<b>c</b>) The PCoA at the ASV level was based on the Bray–Curtis distances between the salivary microbial communities in the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The composition of the salivary microbial communities in the groups at the phylum (total taxa; (<b>a</b>)) and genus (top 100 taxa; (<b>b</b>)) levels. The 16S rRNA gene sequences were input into DADA2 for quality filtering, clustered into ASVs, and the taxa were assigned based on the SILVA v.138.1 database.</p>
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<p>The differentiation of bacteria taxa between the halitosis with periodontitis and periodontally healthy groups was performed using LEfSe analysis. The bacteria in the saliva samples with an LDA score &gt; 2 are displayed. (<b>a</b>) The cladogram generated from the LEfSe analysis revealed distinct microbial clades present in both groups. The colored regions/branches represent discrepancies in the bacterial population structure between the two groups. The green sectors represent clades that are more abundant in the halitosis with periodontitis group compared to the periodontally healthy group, whereas the red sector represents clades that are more abundant in the control group compared to the halitosis with periodontitis group. (<b>b</b>) The LDA scores indicate substantial differences in bacterial composition at the genus level between the halitosis with periodontitis and periodontally healthy groups.</p>
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<p>The principal component analysis (PCA) of the UHPLC-MS metabolic profiles of the saliva samples from the halitosis with periodontitis and periodontally healthy control groups.</p>
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<p>Viability of HSC-4 cells after treatment with indole (<b>a</b>), DMS (<b>b</b>), and H<sub>2</sub>O<sub>2</sub> (<b>c</b>). The cells were incubated with the vehicle (0.1% DMSO or PBS) or various concentrations of indole, DMS, or H<sub>2</sub>O<sub>2</sub> for 24 or 48 h, and cell viability was assessed using the MTT assay. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). Statistical significance was analyzed using one-way ANOVA and followed by Dunnett’s multiple comparisons test: <sup>c</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>b</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>a</sup> <span class="html-italic">p</span> &lt;0.001, and <sup>ns</sup> not significant.</p>
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<p>The effect of indole and DMS on apoptosis and antioxidant and inflammatory properties of HSC-4 cells. The mRNA expression levels of apoptosis- (<b>a</b>), antioxidant- (<b>b</b>), and inflammation-related (<b>c</b>) genes were quantified using RT-qPCR, and the GAPDH gene was used as the reference gene. (<b>d</b>) The relative expressions of IFN-γ and IL-6 were assayed using ELISA. The expression levels of all genes and secreted cytokines of HSC-4 cells after treatment with 100 μg/mL indole and 5 mM DMS were compared to those of untreated control cells. The values are presented as the mean ± SD (<span class="html-italic">n</span> = 3). <sup>c</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>b</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>a</sup> <span class="html-italic">p</span> &lt; 0.001, and <sup>ns</sup> not significant versus vehicle.</p>
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36 pages, 2305 KiB  
Review
Dysbiosis–NK Cell Crosstalk in Pancreatic Cancer: Toward a Unified Biomarker Signature for Improved Clinical Outcomes
by Sara Fanijavadi and Lars Henrik Jensen
Int. J. Mol. Sci. 2025, 26(2), 730; https://doi.org/10.3390/ijms26020730 - 16 Jan 2025
Viewed by 412
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis, primarily due to its immunosuppressive tumor microenvironment (TME), which contributes to treatment resistance. Recent research shows that the microbiome, including microbial communities in the oral cavity, gut, bile duct, and intratumoral environments, [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis, primarily due to its immunosuppressive tumor microenvironment (TME), which contributes to treatment resistance. Recent research shows that the microbiome, including microbial communities in the oral cavity, gut, bile duct, and intratumoral environments, plays a key role in PDAC development, with microbial imbalances (dysbiosis) promoting inflammation, cancer progression, therapy resistance, and treatment side effects. Microbial metabolites can also affect immune cells, especially natural killer (NK) cells, which are vital for tumor surveillance, therapy response and treatment-related side effects. Dysbiosis can affect NK cell function, leading to resistance and side effects. We propose that a combined biomarker approach, integrating microbiome composition and NK cell profiles, can help predict treatment resistance and side effects, enabling more personalized therapies. This review examines how dysbiosis contributes to NK cell dysfunction in PDAC and discusses strategies (e.g., antibiotics, probiotics, vaccines) to modulate the microbiome and enhance NK cell function. Targeting dysbiosis could modulate NK cell activity, improve the effectiveness of PDAC treatments, and reduce side effects. However, further research is needed to develop unified NK cell–microbiome interaction-based biomarkers for more precise and effective patient outcomes. Full article
(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Pancreatic Cancer: 2nd Edition)
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<p>Microbiota–NK cell interaction and PDAC clinical outcome. This figure illustrates the complex interactions between different microbiota (oral, gut, and tumor-associated) and their crosstalk with NK cells in pancreatic ductal adenocarcinoma (PDAC). The microbiota communities not only interact with NK cells but also influence each other; oral microbiota can alter the gut and, ultimately, the tumor-associated microbiota, while tumor-associated microbiota can also modify the gut and oral microbiota. Dysbiosis, a pathological disruption of the microbiota community, can suppress NK cell function, including reducing NK cell frequency, cytotoxicity, and tumor infiltration. In contrast, a balanced microbiota can enhance NK cell activity, promoting tumor surveillance and immune response. Dysbiosis, however, contributes to tumor initiation, progression, resistance to treatment by impairing NK cell-mediated immune responses, and side effects of cancer treatments.</p>
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<p>Microbiota–NK cell interactions and their impact on clinical outcomes. This figure illustrates the various mechanisms through which the microbiota can influence NK cell activity and treatment outcomes in pancreatic ductal adenocarcinoma (PDAC). The diagram highlights the complex interplay between microbiota, NK cells, and chemotherapy. Specifically, it focuses on the AMPK-Bmi1-GATA2-MICA/B pathway, which plays a critical role in tumor immune evasion. Under conditions of energy stress, such as high glucose, AMPK is activated, leading to the upregulation of Bmi1. Bmi1, in turn, activates GATA2, which reduces the expression of MICA/B ligands. These ligands normally stimulate NK cells to recognize and attack tumor cells. By downregulating MICA/B, this pathway helps cancer cells evade NK cell-mediated immune surveillance, thereby promoting tumor growth and resistance to treatment like 5-fluorouracil (5-FU). Gut microbiota metabolites can be broadly categorized into three types based on their origin: (1) metabolites produced directly by the microbiota from dietary components, such as short-chain fatty acids (SCFAs like butyrate) and indole derivatives (like IAA), which play key roles in gut health and immune modulation (via inhibition of histone deacetylases (HDACs)); (2) metabolites generated by the host and modified by the microbiota, like secondary bile acids, which influence metabolism and immune functions; and (3) metabolites produced de novo by the microbiota, such as polysaccharide A (PSA), which helps maintain immune balance. Additionally, some products like uridine, produced by cytidine deaminase (CDA) enzyme, can also be assumed as de novo metabolites. These metabolites, along with other postbiotics like cell-free components (CSF), exert various effects on the host, including modulating inflammation and gut integrity.</p>
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<p>Dysbiosis and targeting dysbiosis in PDAC. This figure summarizes the mechanisms of treatment resistance and side effects in pancreatic ductal adenocarcinoma (PDAC), focusing on the role of microbiota and NK cells. It illustrates how microbiota influence NK cell activity, contributing to both tumor immune evasion and resistance to therapy. Additionally, the figure highlights how microbiota dysbiosis can worsen treatment side effects, such as inflammation and gastrointestinal toxicity. Non-invasive strategies, including dietary changes, probiotics, and fecal microbiota transplantation (FMT), are shown as potential methods to restore microbiome balance, improve therapeutic response, and reduce adverse effects in PDAC.</p>
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<p>Integration of microbiome profiling and NK cell analysis as unified biomarkers for managing PDAC. This figure illustrates a suggested comprehensive diagnostic approach for identifying unified biomarkers to predict treatment resistance and side effects in pancreatic ductal adenocarcinoma (PDAC) patients. The approach integrates microbiota analysis from oral washing samples, stool, or tumor tissues using omics technologies, such as 16S rRNA sequencing, with NK cell profiling. 16S rRNA sequencing is a genomic technique that profiles microbial communities by analyzing the 16S ribosomal RNA gene, providing insights into microbial diversity and how the microbiome may influence PDAC treatment outcomes. The analysis can be performed in PDAC patient cohorts, categorized by long vs. short overall survival, responders vs. non-responders, and patients with vs. without side effects. By combining microbiome data with NK cell profiling, the goal is to identify combined biomarker signatures that optimize clinical outcomes. Additionally, the figure highlights the importance of metagenomics and metaproteomics, which offer complementary insights into the interactions between patient cells and the microbiome, essential for developing robust biomarkers for personalized PDAC therapies. NK cell profiling involves isolating NK cells from patient samples (e.g., peripheral blood, tumor tissues) and analyzing their phenotype, function, and gene expression. This includes flow cytometry to identify NK cell subpopulations (e.g., CD56<sup>dim</sup>CD56<sup>bright</sup>), functional assays (e.g., cytotoxicity and cytokine production), and gene expression profiling (via RNA sequencing or qRT-PCR). These analyses reveal NK cell activation, exhaustion, or senescence, helping to identify biomarkers associated with treatment resistance and side effects, thereby contributing to more personalized PDAC therapies.</p>
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2 pages, 132 KiB  
Abstract
Development of the Breastfed Infant Oral Microbiome over the First Two Years of Life in the BLOSOM Cohort
by Roaa A. Arishi, Ali S. Cheema, Ching T. Lai, Matthew S. Payne, Donna T. Geddes and Lisa F. Stinson
Proceedings 2025, 112(1), 18; https://doi.org/10.3390/proceedings2025112018 - 16 Jan 2025
Viewed by 350
Abstract
Acquisition and development of the oral microbiome are dynamic processes that occur during early life. However, data regarding longitudinal assembly and determinants of the infant oral microbiome are sparse. This study aimed to characterise temporal development of the infant oral microbiome during the [...] Read more.
Acquisition and development of the oral microbiome are dynamic processes that occur during early life. However, data regarding longitudinal assembly and determinants of the infant oral microbiome are sparse. This study aimed to characterise temporal development of the infant oral microbiome during the first two years of life. Infant oral samples (n = 667 samples, 84 infants) were collected at 2–7 days and 1, 2, 3, 4, 5, 6, 9, 12, and 24 months of age using COPAN E-swabs. Bacterial DNA profiles were analysed using full-length 16S rRNA gene sequencing. At 4 months of age, 76.2% of infants were exclusively breastfed, while breastfeeding rates were 83.3% at 6 months and 65.5% at 12 months. The median breastfeeding duration was 12 months (IQR: 3 months). In this cohort, the oral microbiome was dominated by Streptococcus mitis, Gemella haemolysans, and Rothia mucilaginosa. Bacterial richness decreased significantly from 1 to 2 months, then rose significantly from 12 to 24 months. Shannon diversity increased from 1 week to 1 month and again from 6 to 9 months and 9 to 12 months (all p ≤ 0.04). Microbiome composition was significantly associated with multiple factors, including pacifier use, intrapartum antibiotic prophylaxis, maternal allergy, pre-pregnancy BMI, siblings, delivery mode, maternal age, pets at home, and birth season (all p ≤ 0.03). Introduction of solid foods was a significant milestone in oral microbiome development, triggering an increase in bacterial diversity (richness p = 0.0004; Shannon diversity p = 0.0007), a shift in the abundance of seven species, and a change in beta diversity (p = 0.001). These findings underscore how the oral microbiome develops over the first two years of life and highlight the importance of multiple factors, particularly the introduction of solid foods, in shaping the oral microbiome during early life. Full article
25 pages, 1165 KiB  
Review
Iron Homeostasis Dysregulation, Oro-Gastrointestinal Microbial Inflammatory Factors, and Alzheimer’s Disease: A Narrative Review
by Agata Kuziak, Piotr Heczko, Agata Pietrzyk and Magdalena Strus
Microorganisms 2025, 13(1), 122; https://doi.org/10.3390/microorganisms13010122 - 9 Jan 2025
Viewed by 482
Abstract
Alzheimer’s disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder that profoundly impacts cognitive function and the nervous system. Emerging evidence highlights the pivotal roles of iron homeostasis dysregulation and microbial inflammatory factors in the oral and gut microbiome [...] Read more.
Alzheimer’s disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder that profoundly impacts cognitive function and the nervous system. Emerging evidence highlights the pivotal roles of iron homeostasis dysregulation and microbial inflammatory factors in the oral and gut microbiome as potential contributors to the pathogenesis of AD. Iron homeostasis disruption can result in excessive intracellular iron accumulation, promoting the generation of reactive oxygen species (ROS) and oxidative damage. Additionally, inflammatory agents produced by pathogenic bacteria may enter the body via two primary pathways: directly through the gut or indirectly via the oral cavity, entering the bloodstream and reaching the brain. This infiltration disrupts cellular homeostasis, induces neuroinflammation, and exacerbates AD-related pathology. Addressing these mechanisms through personalized treatment strategies that target the underlying causes of AD could play a critical role in preventing its onset and progression. Full article
(This article belongs to the Collection Feature Papers in Medical Microbiology)
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<p>AD pathomechanisms.</p>
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<p>Iron dysregulation and inflammatory factors.</p>
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17 pages, 4794 KiB  
Article
Unveiling Microbiota Profiles in Saliva and Pancreatic Tissues of Patients with Pancreatic Cancer
by Alper Uguz, Can Muftuoglu, Ufuk Mert, Tufan Gumus, Deniz Ece, Milad Asadi, Ozlem Ulusan Bagci and Ayse Caner
Microorganisms 2025, 13(1), 119; https://doi.org/10.3390/microorganisms13010119 - 9 Jan 2025
Viewed by 535
Abstract
The pancreas, previously considered a sterile organ, has recently been shown to harbor its own microbiota that may influence tumor biology and patient outcomes. Despite increasing interest in the impact of the microbiome on cancer, the relationship between pancreatic tissue and oral microbiomes [...] Read more.
The pancreas, previously considered a sterile organ, has recently been shown to harbor its own microbiota that may influence tumor biology and patient outcomes. Despite increasing interest in the impact of the microbiome on cancer, the relationship between pancreatic tissue and oral microbiomes in pancreatic ductal adenocarcinoma (PDAC) remains limited. In this study, the oral and pancreas tissue microbiomes of patients with PDAC were compared to patients with other periampullary cancers (DC/AC) and a healthy control group using 16S rRNA gene sequence analysis. The results showed a significant reduction in microbial diversity in the saliva of cancer patients compared to healthy controls, while the PDAC patients exhibited a distinct microbial profile in their pancreatic tissues, consisting predominantly of Firmicutes, Proteobacteria, and Actinobacter, after filtering the microbiome of the indoor environment. Notably, the presence of oral bacteria such as Anoxybacillus, Clostridium, and Bacillus in pancreatic tissues suggests potential translocation from the oral cavity. This study emphasizes the importance of understanding the role of body fluid and tissue microbiota in pancreatic cancer, proposing that oral dysbiosis may contribute to disease progression. Moreover, the results suggest that the microbiome of the indoor environment in which samples are collected and analyzed is also important in microbiota analysis studies. Full article
(This article belongs to the Collection Microbiomes and Cancer: A New Era in Diagnosis and Therapy)
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<p>Inclusion criteria of study groups and sampling flow chart.</p>
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<p>Coverage of taxonomies in saliva and pancreatic tissue samples. Venn diagrams illustrate overlap of OTUs in microbiota of saliva (<b>A</b>) and pancreatic tissue (<b>B</b>) samples. Red circle means periampullary cancer including distal cholangiocarcinoma/ampullary cancer (DC/AC), gray circle means pancreatic ductal adenocarcinoma (PDAC), and blue circle means healthy control (HC). Alpha diversity shows differences in saliva samples (<b>C</b>) and tissue samples (<b>D</b>) of PDAC, DCC, and HC groups. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 indicates significant values in the graph, not significant value isn’t shown in the graph.</p>
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<p>The microbiome profiles in the samples. The relative abundances of each phylum in the saliva samples (<b>A</b>) and pancreatic tissue samples (<b>B</b>) are shown. (<b>A</b>) The relative abundances of each genus in the saliva samples (<b>C</b>) and pancreatic tissue samples (<b>D</b>) are shown.</p>
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<p>Relative abundance of significantly enriched species in saliva samples of the three groups: pancreatic ductal adenocarcinoma (PDAC) (gray circle), distal cholangiocarcinoma/ampullary cancer (DC/AC) (red circle), and healthy control (HC) (blue circle). *, **, ***, and **** represent <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001, and <span class="html-italic">p</span> &lt; 0.0001, respectively.</p>
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<p>Relative abundance of significantly enriched species in pancreatic tissue samples of the cancer patients: pancreatic ductal adenocarcinoma (PDAC) (red circle) and distal cholangiocarcinoma/ampullary cancer (DC/AC) (green circle). * and ** represent <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>The microbiome profiles in the saliva and pancreatic tissue samples of matched cancer patients. The distributions of significant bacterial abundance at the genus (<b>A</b>,<b>B</b>) and species (<b>C</b>,<b>D</b>) levels are shown.</p>
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<p>Relative abundance of top bacteria at phylum (<b>A</b>) and genus (<b>B</b>,<b>C</b>) levels, according to indoor environments in each sample collection room. Saline—OR (operating room), Saline—SCR (sample collection room).</p>
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<p>The summary of oral and pancreatic tissue microbiomes in PDAC, DC/AC, and HC groups. The direction of the arrows in the boxes shows the increase and decrease in bacterial abundance.</p>
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16 pages, 1239 KiB  
Review
Periodontal Disease and Obstructive Sleep Apnea: Shared Mechanisms, Clinical Implications, and Future Research Directions
by Serena Incerti Parenti, Claudio Cesari, Veronica Della Godenza, Matteo Zanarini, Francesca Zangari and Giulio Alessandri Bonetti
Appl. Sci. 2025, 15(2), 542; https://doi.org/10.3390/app15020542 - 8 Jan 2025
Viewed by 473
Abstract
This review explores the emerging relationship between obstructive sleep apnea (OSA) and periodontal disease (PD), emphasizing shared inflammatory pathways, overlapping risk factors, and potential systemic health implications. Both conditions are characterized by chronic inflammation and oxidative stress, which independently contribute to cardiovascular disease, [...] Read more.
This review explores the emerging relationship between obstructive sleep apnea (OSA) and periodontal disease (PD), emphasizing shared inflammatory pathways, overlapping risk factors, and potential systemic health implications. Both conditions are characterized by chronic inflammation and oxidative stress, which independently contribute to cardiovascular disease, diabetes, and other systemic disorders. Evidence suggests a bidirectional relationship, with OSA-related hypoxia exacerbating periodontal tissue breakdown and PD-induced inflammation potentially influencing OSA severity. However, the causative nature of the relationship between OSA and PD remains uncertain, largely due to inconsistencies in diagnostic criteria, methodological variability, and study heterogeneity. This review highlights the essential role of systematic reviews (SRs) in synthesizing current evidence, identifying research gaps, and guiding future studies. To maximize their impact, SRs should adhere to rigorous methodological quality standards, improve transparency in data reporting, and address the heterogeneity of included studies. Future research should focus on longitudinal and interventional designs, standardize diagnostic protocols, and investigate biomarkers, oral microbiome profiles, and inflammatory mediators to elucidate the mechanisms linking OSA and PD. Multidisciplinary collaboration between dental and sleep specialists is crucial to advancing evidence-based strategies that improve patient outcomes and address the broader health implications of these often coexisting conditions. Full article
(This article belongs to the Section Applied Dentistry and Oral Sciences)
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<p>Overview diagram illustrating the topics addressed in this narrative review.</p>
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<p>AMSTAR-2 response rates by item. Y = yes, PY = partial yes, N = no, NMC = no meta-analysis conducted.</p>
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32 pages, 1321 KiB  
Review
Shattering the Amyloid Illusion: The Microbial Enigma of Alzheimer’s Disease Pathogenesis—From Gut Microbiota and Viruses to Brain Biofilms
by Anna Onisiforou, Eleftheria G. Charalambous and Panos Zanos
Microorganisms 2025, 13(1), 90; https://doi.org/10.3390/microorganisms13010090 - 5 Jan 2025
Viewed by 1893
Abstract
For decades, Alzheimer’s Disease (AD) research has focused on the amyloid cascade hypothesis, which identifies amyloid-beta (Aβ) as the primary driver of the disease. However, the consistent failure of Aβ-targeted therapies to demonstrate efficacy, coupled with significant safety concerns, underscores the need to [...] Read more.
For decades, Alzheimer’s Disease (AD) research has focused on the amyloid cascade hypothesis, which identifies amyloid-beta (Aβ) as the primary driver of the disease. However, the consistent failure of Aβ-targeted therapies to demonstrate efficacy, coupled with significant safety concerns, underscores the need to rethink our approach to AD treatment. Emerging evidence points to microbial infections as environmental factors in AD pathoetiology. Although a definitive causal link remains unestablished, the collective evidence is compelling. This review explores unconventional perspectives and emerging paradigms regarding microbial involvement in AD pathogenesis, emphasizing the gut–brain axis, brain biofilms, the oral microbiome, and viral infections. Transgenic mouse models show that gut microbiota dysregulation precedes brain Aβ accumulation, emphasizing gut–brain signaling pathways. Viral infections like Herpes Simplex Virus Type 1 (HSV-1) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) may lead to AD by modulating host processes like the immune system. Aβ peptide’s antimicrobial function as a response to microbial infection might inadvertently promote AD. We discuss potential microbiome-based therapies as promising strategies for managing and potentially preventing AD progression. Fecal microbiota transplantation (FMT) restores gut microbial balance, reduces Aβ accumulation, and improves cognition in preclinical models. Probiotics and prebiotics reduce neuroinflammation and Aβ plaques, while antiviral therapies targeting HSV-1 and vaccines like the shingles vaccine show potential to mitigate AD pathology. Developing effective treatments requires standardized methods to identify and measure microbial infections in AD patients, enabling personalized therapies that address individual microbial contributions to AD pathogenesis. Further research is needed to clarify the interactions between microbes and Aβ, explore bacterial and viral interplay, and understand their broader effects on host processes to translate these insights into clinical interventions. Full article
(This article belongs to the Special Issue Latest Review Papers in Medical Microbiology 2024)
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<p><b>Illustration of the multifaceted interactions between various microbial communities and AD pathogenesis.</b> Gut microbiome dysbiosis, with bacterial amyloids like curli produced by <span class="html-italic">Escherichia coli</span> and <span class="html-italic">Pseudomonas aeruginosa</span>, can travel to the brain via the bloodstream or vagus nerve, contributing to Aβ protein aggregation in the brain. Brain microbiota can also lead to the formation of amyloid-containing brain biofilms, further contributing to Aβ protein aggregation. Bacterial or viral infections in the brain activate microglia and trigger neuroinflammation, releasing pro-inflammatory cytokines that exacerbate AD pathology. Oral microbiome dysbiosis and periodontal disease also contribute to AD progression by promoting inflammation and possibly introducing pathogens into the brain.</p>
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<p><b>Illustration of personalized treatment approaches based on microbiome-based and antiviral therapies for AD.</b> The diagnostic protocol for pathobiome involves standardized methods to assess microbial infections in AD, including pathogen identification and drug susceptibility testing. Based on these diagnostic results, personalized treatment strategies can be developed. These include FMT from a healthy, non-cognitively-impaired individual to restore a balanced gut microbiome, the use of probiotics and prebiotics to introduce beneficial microbiota and support microflora health, and antiviral treatments targeting acute and chronic viral infections with considerations for safety and efficacy. These strategies aim to leverage the benefits of microbiome modulation to mitigate AD pathology.</p>
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14 pages, 3425 KiB  
Article
Association of Corticosteroid Inhaler Type with Saliva Microbiome in Moderate-to-Severe Pediatric Asthma
by Amir Hossein Alizadeh Bahmani, Mahmoud I. Abdel-Aziz, Simone Hashimoto, Corinna Bang, Susanne Brandstetter, Paula Corcuera-Elosegui, Andre Franke, Mario Gorenjak, Susanne Harner, Parastoo Kheiroddin, Leyre López-Fernández, Anne H. Neerincx, Maria Pino-Yanes, Uroš Potočnik, Olaia Sardón-Prado, Antoaneta A. Toncheva, Christine Wolff, Michael Kabesch, Aletta D. Kraneveld, Susanne J. H. Vijverberg, Anke H. Maitland-van der Zee and on behalf of the SysPharmPediA consortiumadd Show full author list remove Hide full author list
Biomedicines 2025, 13(1), 89; https://doi.org/10.3390/biomedicines13010089 - 2 Jan 2025
Viewed by 796
Abstract
Background/Objectives: Metered-dose inhalers (MDIs) and dry powder inhalers (DPIs) are common inhaled corticosteroid (ICS) inhaler devices. The difference in formulation and administration technique of these devices may influence oral cavity microbiota composition. We aimed to compare the saliva microbiome in children with [...] Read more.
Background/Objectives: Metered-dose inhalers (MDIs) and dry powder inhalers (DPIs) are common inhaled corticosteroid (ICS) inhaler devices. The difference in formulation and administration technique of these devices may influence oral cavity microbiota composition. We aimed to compare the saliva microbiome in children with moderate-to-severe asthma using ICS via MDIs versus DPIs. Methods: Saliva samples collected from 143 children (6–17 yrs) with moderate-to-severe asthma across four European countries (The Netherlands, Germany, Spain, and Slovenia) as part of the SysPharmPediA cohort were subjected to 16S rRNA sequencing. The microbiome was compared using global diversity (α and β) between two groups of participants based on inhaler devices (MDI (n = 77) and DPI (n = 65)), and differential abundance was compared using the Analysis of Compositions of Microbiomes with the Bias Correction (ANCOM-BC) method. Results: No significant difference was observed in α-diversity between the two groups. However, β-diversity analysis revealed significant differences between groups using both Bray–Curtis and weighted UniFrac methods (adjusted p-value = 0.015 and 0.044, respectively). Significant differential abundance between groups, with higher relative abundance in the MDI group compared to the DPI group, was detected at the family level [Carnobacteriaceae (adjusted p = 0.033)] and at the genus level [Granulicatella (adjusted p = 0.021) and Aggregatibacter (adjusted p = 0.011)]. Conclusions: Types of ICS devices are associated with different saliva microbiome compositions in moderate-to-severe pediatric asthma. The causal relation between inhaler types and changes in saliva microbiota composition needs to be further evaluated, as well as whether this leads to different potential adverse effects in terms of occurrence and level of severity. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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<p>Descriptive study flow chart. Participants were categorized based on the type of inhaled corticosteroid device: DPI: dry powder inhalers; MDI: metered-dose inhalers.</p>
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<p>DAG (directed acyclic graph). Created by <a href="http://www.dagitty.net" target="_blank">www.dagitty.net</a>. ICS: inhaled corticosteroid.</p>
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<p>Relative abundance of samples at the phylum (<b>A</b>), family (<b>B</b>), and genus (<b>C</b>) levels between the children using DPI and children using MDI. DPI: dry powder inhalers; MDI: metered-dose inhalers.</p>
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<p>Relative abundance of samples at the phylum (<b>A</b>), family (<b>B</b>), and genus (<b>C</b>) levels between the children using DPI and children using MDI. DPI: dry powder inhalers; MDI: metered-dose inhalers.</p>
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<p>Bacterial taxa with significant differential abundance between children using DPI (in red) and children using MDI (in blue) at the family (<b>A</b>) and genus (<b>B</b>) levels. DPI: dry powder inhalers; MDI: metered-dose inhalers.</p>
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11 pages, 1932 KiB  
Case Report
Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis
by Claire A. Shaw, Maria Soltero-Rivera, Rodrigo Profeta and Bart C. Weimer
Bacteria 2025, 4(1), 1; https://doi.org/10.3390/bacteria4010001 - 2 Jan 2025
Viewed by 590
Abstract
The cat oral microbiome plays an important role in maintaining host health, yet little is known about how to apply microbial data in a clinical setting. One such use of microbiome signatures is in cases of feline chronic gingivostomatitis (FCGS), a severe debilitating [...] Read more.
The cat oral microbiome plays an important role in maintaining host health, yet little is known about how to apply microbial data in a clinical setting. One such use of microbiome signatures is in cases of feline chronic gingivostomatitis (FCGS), a severe debilitating complex disease of the oral cavity. FCGS-afflicted cats have limited treatment options, and individual patient responses to treatment are needed. In this work, we used deep sequencing of total RNA of the oral microbiome to chronicle microbial changes that accompanied an FCGS-afflicted cat’s change from treatment-non-responsive to treatment-responsive within a 17-month span. The oral microbiome composition of the two treatment-non-responsive time points differed from that of the treatment-responsive point, with notable shifts in the abundance of Myscoplasmopsis, Aspergillus, and Capnocytophaga species. Intriguingly, the presence of the fungal groups Aspergillus and Candida primarily differentiated the two non-responsive microbiomes. Associated with responder status were multiple Capnocytophaga species, including Capnocytophaga sp. H2931, Capnocytophaga gingivalis, and Capnocytophaga canimorsus. The observation that the oral microbiome shifts in tandem by response to treatment in FCGS suggests a potential use for microbiome evaluations in a clinical setting. This work contributes to developing improved molecular diagnostics for enhanced efficacy of individualized treatment plans to improve oral disease. Full article
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<p>Clinical pictures of the oral cavity across three visits spanning 17 months. Panel (<b>A</b>) depicts the patient at initial diagnosis of feline chronic gingivostomatitis (FCGS). Panel (<b>B</b>) depicts substantial improvement seen three months post FM extraction; however, inflammation recurred 5 months later, requiring the start of immunosuppressive therapy. Panel (<b>C</b>) depicts the transition to responsive and adequately managed with prednisolone treatment 10 months into the treatment and 6 months into tapering with further tapering recommended.</p>
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<p>Overview of study methods. A single cat was seen during three clinical examinations across three years. During the first two examinations, the cat was classified as non-responsive or refractory for tooth extractions. The third visit saw the cat reclassified as responsive due to remission of inflammatory symptoms. Total RNA was extracted from each of the three swab samples, sequenced, and assigned taxonomy at the species level.</p>
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<p>Oral microbiome composition shifts both across sampling time and by treatment response status. Venn diagram comparing the shared and unique species across the three sampling points which encompassed two refractory points and a shift to responder status.</p>
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<p>Microbial population shifts in the microbiome correlate to refractory or responder status in the oral microbiome of a cat with FCGS. (<b>A</b>) Alluvial plot displaying genus composition on the strata and species changes on the alluvia. Organisms with less than 10% proportion in the microbiome were collapsed into the ‘Mixed’ box. (<b>B</b>) Correlation plot illustrating the shared and unique organisms in the aggregated refractory microbiome compared to the responder microbiome. Each dot represents a single organism, with the center diagonal indicating equal abundance in the two groupings. The red line on the top indicates organisms that are unique to the responder condition. The teal line to the right indicates the organisms unique to refractory condition. The boxes on each of those lines are expansions to provide names of top 10 individual organisms in that condition.</p>
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<p>Refractory and responder microbiomes differ in the proportion of eukaryotic and bacterial microorganisms. Kingdom labels from the Kraken2 assignment were used to plot the relative abundance of bacteria and of eukaryotic organisms in the aggregated refractory associated microbiome (<b>left</b>) and in the responder microbiome (<b>right</b>). Cat host reads were removed prior to taxonomic assignment of microbes and thus are not represented in the eukaryotic load plotted here.</p>
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