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23 pages, 1368 KiB  
Review
Microbiome-Driven Therapeutics: From Gut Health to Precision Medicine
by Muneer Oladipupo Yaqub, Aashika Jain, Chinedu Eucharia Joseph and Lekshmi K. Edison
Gastrointest. Disord. 2025, 7(1), 7; https://doi.org/10.3390/gidisord7010007 - 15 Jan 2025
Viewed by 101
Abstract
The human microbiome, a complex ecosystem of microorganisms residing in and on the body, plays a pivotal role in the regulation of a wide range of physiological processes, including digestion, immune responses, and metabolic functions. In recent years, the rapidly growing field of [...] Read more.
The human microbiome, a complex ecosystem of microorganisms residing in and on the body, plays a pivotal role in the regulation of a wide range of physiological processes, including digestion, immune responses, and metabolic functions. In recent years, the rapidly growing field of microbiome-driven therapeutics has garnered significant attention owing to its potential to revolutionize healthcare. This review explores the evolving landscape of microbiome-based therapies, with a particular focus on the gut microbiome and its implications for both gut health and precision medicine. We highlight recent advances in understanding how microbial communities influence disease pathogenesis and treatment outcomes, spanning conditions such as inflammatory bowel disease (IBD), metabolic disorders, neurological diseases, and even cancer. This article also discusses emerging therapeutic strategies, including probiotics, prebiotics, fecal microbiota transplantation (FMT), and microbial-based drugs, as well as the challenges associated with their clinical implementation. Additionally, we examined how the integration of microbiome profiling and metagenomic data is advancing the field of precision medicine, paving the way for personalized and effective treatments. This review serves as a comprehensive resource that synthesizes current knowledge, identifies key gaps in microbiome research, and offers insights into the future direction of microbiome-driven therapeutics, thus providing a valuable framework for clinicians, researchers, and policymakers seeking to harness the potential of microbiomes to advance personalized healthcare solutions. Full article
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<p>Gut microbiota and disease connections (Created in <a href="https://BioRender.com" target="_blank">https://BioRender.com</a>).</p>
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<p>Gut microbiota restoration through fecal microbiota transplantation (FMT) in patients with <span class="html-italic">Clostridioides difficile</span> infection (Created in <a href="https://BioRender.com" target="_blank">https://BioRender.com</a>).</p>
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14 pages, 670 KiB  
Review
The Role and the Regulation of NLRP3 Inflammasome in Irritable Bowel Syndrome: A Narrative Review
by Arezina Kasti, Konstantinos Katsas, Maroulla D. Nikolaki and Konstantinos Triantafyllou
Microorganisms 2025, 13(1), 171; https://doi.org/10.3390/microorganisms13010171 - 15 Jan 2025
Viewed by 145
Abstract
Irritable bowel syndrome (IBS) is a chronic disorder of the gastrointestinal tract. Its pathogenesis involves multiple factors, including visceral hypersensitivity and immune activation. NLRP3 inflammasome is part of the nucleotide-binding oligomerization domain-like receptor (NLR) family, a crucial component of the innate immune system. [...] Read more.
Irritable bowel syndrome (IBS) is a chronic disorder of the gastrointestinal tract. Its pathogenesis involves multiple factors, including visceral hypersensitivity and immune activation. NLRP3 inflammasome is part of the nucleotide-binding oligomerization domain-like receptor (NLR) family, a crucial component of the innate immune system. Preclinical studies have demonstrated that inhibiting NLRP3 reduces visceral sensitivity and IBS symptoms, like abdominal pain, and diarrhea, suggesting that targeting the NLRP3 might represent a novel therapeutic approach for IBS. This review aims to assess the NLRP3 inhibitors (tranilast, β-hydroxybutyrate, Chang-Kang-fang, paeoniflorin, coptisine, BAY 11-7082, and Bifidobacterium longum), highlighting the signaling pathways, and their potential role in IBS symptoms management was assessed. Although premature, knowledge of the action of synthetic small molecules, phytochemicals, organic compounds, and probiotics might make NLRP3 a new therapeutic target in the quiver of physicians’ therapeutic choices for IBS symptoms management. Full article
(This article belongs to the Section Gut Microbiota)
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<p>The NLRP3 inflammasome acts as a sensor–adaptor–effector system that detects cell stress and membrane damage. Normally, NLRP3 levels are insufficient to activate myeloid immune cells, keeping ASC and caspase-1 stable [<a href="#B8-microorganisms-13-00171" class="html-bibr">8</a>]. Upon activation, ASC and caspase-1 form a complex with NLRP3, activating caspase-1, which then converts pro-inflammatory cytokines IL-1β and IL-18 into their active forms, triggering an inflammatory response [<a href="#B5-microorganisms-13-00171" class="html-bibr">5</a>]. Canonical activation of the NLRP3 inflammasome involves two signals: priming (Signal 1), which increases levels of its components in response to PAMPs or DAMPs, and oligomerization (Signal 2), which occurs due to stress signals like ion disturbances and mitochondrial reactive oxygen species. The activation of NLRP3 results in its aggregation with ASC and pro-caspase-1, which leads to the maturation of interleukin-1 beta (IL-1β) and interleukin-18 (IL-18) as well as the cleavage of gasdermin D (GSDMD). This cleavage triggers pyroptosis. In IBS, PAMPs are considered the dysbiotic gut microbiota (and the bacterial toxins) that trigger NLRP3 activation [<a href="#B9-microorganisms-13-00171" class="html-bibr">9</a>]. The figure was designed using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.</p>
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14 pages, 1020 KiB  
Article
Probiotic Therapy as an Adjuvant in the Treatment of Periodontal Disease: An Innovative Approach
by Liliana Sachelarie, Ioana Scrobota, Ioana Romanul, Raluca Iurcov, Georgiana Ioana Potra Cicalau and Liana Todor
Medicina 2025, 61(1), 126; https://doi.org/10.3390/medicina61010126 - 14 Jan 2025
Viewed by 359
Abstract
Background and Objectives: Periodontal inflammation, often linked to oral microbiota dysbiosis dominated by pathogenic bacteria, remains a significant challenge in periodontitis management. Traditional periodontal therapies primarily reduce the bacterial load but fail to restore the microbiota balance. Probiotics offer a promising therapeutic [...] Read more.
Background and Objectives: Periodontal inflammation, often linked to oral microbiota dysbiosis dominated by pathogenic bacteria, remains a significant challenge in periodontitis management. Traditional periodontal therapies primarily reduce the bacterial load but fail to restore the microbiota balance. Probiotics offer a promising therapeutic adjunct with their ability to enhance beneficial bacteria. This study investigates the effects of probiotics on the oral microbiota, inflammatory markers (IL-1β, TNF-α), and clinical parameters (gingival index, bleeding index, and periodontal pocket depth). Materials and Methods: In this pilot study, 80 patients with moderate-to-severe periodontitis were assigned to two groups. Group A received standard periodontal therapy (non-surgical periodontal therapy (NSPT)) with probiotic supplementation (Lactobacillus reuteri, 2 × 10⁹ CFU daily for 8 weeks), and Group B received standard treatment with a placebo. Microbiological changes were assessed via quantitative PCR, while inflammatory markers (IL-1β, TNF-α) were analyzed using ELISA. Clinical parameters, including the gingival index (GI), bleeding index (BI), and periodontal pocket depth (PPD), were measured at baseline (T0), 4 weeks (T1), and 8 weeks (T2) using standardized methods. Results: Probiotic therapy (Group A) significantly reduced the pathogenic bacteria and increased the beneficial bacteria levels compared to the placebo (p < 0.01). Inflammatory markers decreased by 37% (IL-1β) and 42% (TNF-α), while clinical parameters improved, with reductions in the gingival and bleeding indices (−1.5, −1.3) and a 2 mm decrease in the periodontal pocket depth (p < 0.01). Conclusions: Probiotics, as an adjunct to periodontal therapy, effectively restore the microbiota balance, reduce inflammation, and improve clinical outcomes in periodontitis. Full article
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<p>CONSORT flow diagram.</p>
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<p>Reduction in GI and PPD with <span class="html-italic">p</span>-values-group A.</p>
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<p>Reduction in GI and PPD with <span class="html-italic">p</span>-values—Group B.</p>
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<p>Comparison of GI and PPD between Groups A and B at T2.</p>
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19 pages, 4583 KiB  
Article
Changes in RNA Splicing: A New Paradigm of Transcriptional Responses to Probiotic Action in the Mammalian Brain
by Xiaojie Yue, Lei Zhu and Zhigang Zhang
Microorganisms 2025, 13(1), 165; https://doi.org/10.3390/microorganisms13010165 - 14 Jan 2025
Viewed by 346
Abstract
Elucidating the gene regulatory mechanisms underlying the gut–brain axis is critical for uncovering novel gut–brain interaction pathways and developing therapeutic strategies for gut bacteria-associated neurological disorders. Most studies have primarily investigated how gut bacteria modulate host epigenetics and gene expression; their impact on [...] Read more.
Elucidating the gene regulatory mechanisms underlying the gut–brain axis is critical for uncovering novel gut–brain interaction pathways and developing therapeutic strategies for gut bacteria-associated neurological disorders. Most studies have primarily investigated how gut bacteria modulate host epigenetics and gene expression; their impact on host alternative splicing, particularly in the brain, remains largely unexplored. Here, we investigated the effects of the gut-associated probiotic Lacidofil® on alternative splicing across 10 regions of the rat brain using published RNA-sequencing data. The Lacidofil® altogether altered 2941 differential splicing events, predominantly, skipped exon (SE) and mutually exclusive exon (MXE) events. Protein–protein interactions and a KEGG analysis of differentially spliced genes (DSGs) revealed consistent enrichment in the spliceosome and vesicle transport complexes, as well as in pathways related to neurodegenerative diseases, synaptic function and plasticity, and substance addiction across brain regions. Using the PsyGeNET platform, we found that DSGs from the locus coeruleus (LConly), medial preoptic area (mPOA), and ventral dentate gyrus (venDG) were enriched in depression-associated or schizophrenia-associated genes. Notably, we highlight the App gene, where Lacidofil® precisely regulated the splicing of two exons causally involved in amyloid β protein-based neurodegenerative diseases. Although the splicing factors exhibited both splicing plasticity and expression plasticity in response to Lacidofil®, the overlap between DSGs and differentially expressed genes (DEGs) in most brain regions was rather low. Our study provides novel mechanistic insight into how gut probiotics might influence brain function through the modulation of RNA splicing. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>Probiotic-induced alternative splicing changes across the rat brain regions. (<b>A</b>,<b>B</b>) The number (<b>A</b>) and percentage (<b>B</b>) of the five types of significantly (<span class="html-italic">p</span> &lt; 0.05) different alternative splicing events (bar color) are shown. AS is the abbreviation of alternative splicing.</p>
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<p>Shared DSGs across brain regions. (<b>A</b>) Gene family distribution of DSGs shared by at least two brain regions. The bar colors represent the types of gene families, and the bar heights represent the number of shared brain regions. (<b>B</b>) Distribution of genes showing widespread splicing pattern changes across brain regions. The heights of the blue bars represent the number of shared brain regions.</p>
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<p>The distribution and classification of GO terms enriched in DSGs across brain regions. The colors in the leftmost bars denote different GO types and their function classes. The heights of the rightmost bars represent the number of shared brain regions.</p>
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<p>The distribution and classification of KEGG pathways enriched in DSGs across brain regions. The colors in the leftmost bars denote different function classes of KEGG pathways. The heights of the rightmost bars represent the number of shared brain regions.</p>
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<p>Visualization of densely connected networks for DSGs in dorDG, mPOA, PLC, Raphe and venDG. The MCODE modules highlighted with red nodes are involved in RNA splicing, while those with green nodes are associated with vesicle transport. The red or green text annotations denote the biological functions of the adjacent networks.</p>
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<p>The validated associations between DSGs and psychiatric disorders. (<b>A</b>) The colors of the bubbles indicate the types of psychiatric disorders, while the sizes of the bubbles represent the number of associated genes. (<b>B</b>) The significance of associations between DSGs and psychiatric disorders. The colors of the squares represent the <span class="html-italic">p</span>-value. * <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>Visualization of splice junctions in alternative splicing events. Ten sashimi plots denote biological replicates for the control group (red) and probiotic group (blue). Numbers on the curved lines indicate the count of junction-spanning reads. The inclusion level (IncLevel) of each biological replicate, the differential average inclusion level between the control and probiotic groups (IncLevelDifference), and the corresponding <span class="html-italic">p</span>-value are demonstrated in the figure. The schematic diagrams at the top indicate the types of RNA splicing events. The black tracks at the bottom indicate the genomic locations of splicing events.</p>
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<p>Expression profiles of selected splicing factors in the dorDG and ILC regions. Bean plots show the differential expression levels of splicing factor-encoding genes (<span class="html-italic">Prpf40a</span>, <span class="html-italic">Srsf10</span>, <span class="html-italic">U2af1l4</span>, <span class="html-italic">Hnrnpab</span>, and <span class="html-italic">Hnrnpa2b1</span>) between the control and probiotic groups. Black lines indicate the median values; white lines represent the data points; and polygons illustrate the estimated density of the data.</p>
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32 pages, 1181 KiB  
Review
Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease
by Magdalini Kreouzi, Nikolaos Theodorakis, Maria Nikolaou, Georgios Feretzakis, Athanasios Anastasiou, Konstantinos Kalodanis and Aikaterini Sakagianni
Microorganisms 2025, 13(1), 161; https://doi.org/10.3390/microorganisms13010161 - 14 Jan 2025
Viewed by 358
Abstract
Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier [...] Read more.
Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier disruption, immune dysregulation, and increased susceptibility to inflammatory skin diseases, including psoriasis, atopic dermatitis, and acne. For instance, hyperglycemia in T2DM leads to the formation of advanced glycation end products (AGEs), which bind to the receptor for AGEs (RAGE) on keratinocytes and immune cells, promoting oxidative stress and inflammation while facilitating Staphylococcus aureus colonization in atopic dermatitis. Similarly, obesity-induced dysregulation of sebaceous lipid composition increases saturated fatty acids, favoring pathogenic strains of Cutibacterium acnes, which produce inflammatory metabolites that exacerbate acne. Advances in metabolomics and microbiome sequencing have unveiled critical biomarkers, such as short-chain fatty acids and microbial signatures, predictive of therapeutic outcomes. For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. Emerging therapies, such as probiotics and postbiotics, aim to restore microbial diversity, while phage therapy selectively targets pathogenic bacteria like Staphylococcus aureus without disrupting beneficial flora. Clinical trials have demonstrated significant reductions in inflammatory lesions and improved quality-of-life metrics in patients receiving these microbiota-targeted treatments. This review synthesizes current evidence on the bidirectional interplay between metabolic disorders and skin microbiota, highlighting therapeutic implications and future directions. By addressing systemic metabolic dysfunction and microbiota-mediated pathways, precision strategies are paving the way for improved patient outcomes in dermatologic care. Full article
(This article belongs to the Special Issue Human Skin Microbiota, 2nd Edition)
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<p>Interconnected pathways linking metabolic disorders to skin microbiome dysbiosis and cutaneous disease. Metabolic disorders influence skin health and the microbiome through interconnected mechanisms. Chronic low-grade inflammation, or meta-inflammation, driven by pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β, disrupts keratinocyte differentiation, weakens the epidermal barrier, and alters AMP production, increasing susceptibility to infections and dysbiosis. Immune dysregulation plays a significant role, as adipokines like leptin promote Th1/Th17 polarization, while reduced adiponectin removes anti-inflammatory control, intensifying immune activation and microbial imbalances. Neurovascular dysregulation, a notable mechanism in rosacea, is driven by the increased activation of pathways such as TRPV1 channels and exacerbates skin sensitivity and dysbiosis. Microvascular dysfunction and reduced capillary perfusion create hypoxic conditions that favor anaerobic or facultative anaerobic microbes, altering microbial ecology. Dysregulated lipid metabolism, particularly altered sebaceous gland activity in insulin resistance, leads to changes in sebum composition, such as increased saturated fatty acids, which promote the colonization of pathogenic microbes and disrupt the balance of commensal microbes. Systemic nutritional and metabolic influences, including hyperglycemia and dyslipidemia, provide substrates for microbial growth, destabilizing skin homeostasis. Oxidative stress and lipid peroxidation further damage keratinocytes and lipids, compromising skin integrity and promoting microbial overgrowth. Sebaceous gland hyperactivity, induced by hyperinsulinemia and IGF-1, stimulates excessive lipid production, creating a nutrient-rich environment for opportunistic microbes. Cytokines and oxidative stress reduce the expression of barrier proteins like filaggrin and involucrin, increasing transepidermal water loss and weakening physical defenses against microbial invasion. AGEs, formed under hyperglycemic conditions, bind to their receptor RAGE, triggering NF-κB-mediated inflammation and oxidative stress. This process impairs skin barrier proteins, disrupts collagen cross-linking, and affects keratinocyte function. These mechanisms collectively illustrate how metabolic disorders create both systemic and localized environments conducive to skin dysbiosis, inflammation, and disease, underscoring the need for integrated therapeutic strategies targeting metabolic dysfunction and skin health. Systemic effects are marked in blue, while localized effects are marked in orange.</p>
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<p>Flowchart of the complex interactions between AGE-RAGE pathway and the skin. Abbreviations. AGEs (Advanced Glycation End Products); AMP (Antimicrobial Peptides); NF-κB (Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B cells); RAGE (Receptor for Advanced Glycation End Products); ROS (Reactive Oxygen Species); TEWL (Transepidermal Water Loss); ↑ (increased); ↓ (decreased).</p>
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11 pages, 961 KiB  
Review
Review of Streptococcus salivarius BLIS K12 in the Prevention and Modulation of Viral Infections
by John R. Tagg, Liam K. Harold and John D. F. Hale
Appl. Microbiol. 2025, 5(1), 7; https://doi.org/10.3390/applmicrobiol5010007 - 14 Jan 2025
Viewed by 275
Abstract
The discovery and application of bacteriocin-producing probiotics, such as Streptococcus salivarius K12 (BLIS K12), represent significant advances in the prevention and management of bacterial infections, particularly in the oral cavity and upper respiratory tract. Originally developed for its bacteriocin-mediated inhibition of the important [...] Read more.
The discovery and application of bacteriocin-producing probiotics, such as Streptococcus salivarius K12 (BLIS K12), represent significant advances in the prevention and management of bacterial infections, particularly in the oral cavity and upper respiratory tract. Originally developed for its bacteriocin-mediated inhibition of the important bacterial pathogen Streptococcus pyogenes, BLIS K12 has more recently also demonstrated potential in the modulation and prevention of viral infections, including COVID-19. Emerging evidence also suggests a broader role for BLIS K12 in immune regulation, with implications for controlling hyperinflammatory responses and enhancing mucosal immunity. Of particular interest is recent work indicating that BLIS K12 can modulate antibody responses against viral antigens, such as the SARS-CoV-2 spike protein, positioning it as a unique adjunct in managing viral infections. This review chronicles the pathway of BLIS K12’s probiotic development, emphasizing its relevant bacteriocin mechanisms, oral health applications, emerging antiviral properties, and potential broader health benefits through immune modulation, all of which position it as a significant non-pharmacological adjunct in managing respiratory and immune health Full article
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<p>Summary of the mechanisms BLIS K12 utilizes to modulate the immune system to protect against viral pathogens. Created in BioRender. Hale, J. (2025); <a href="https://BioRender.com/k63a780" target="_blank">https://BioRender.com/k63a780</a> (Accessed 10 January 2025).</p>
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<p>Two proposed mechanisms of BLIS K12 in disruption of SARS-CoV-2 infectivity. Created in BioRender. Hale, J. (2025); <a href="https://app.biorender.com/citation/67803a2d275900444cb54667" target="_blank">https://app.biorender.com/citation/67803a2d275900444cb54667</a> (Accessed 10 January 2025).</p>
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21 pages, 2387 KiB  
Article
Characterization and Probiotic Potential of Levilactobacillus brevis DPL5: A Novel Strain Isolated from Human Breast Milk with Antimicrobial Properties Against Biofilm-Forming Staphylococcus aureus
by Ivan Iliev, Galina Yahubyan, Elena Apostolova-Kuzova, Mariyana Gozmanova, Daniela Mollova, Iliya Iliev, Lena Ilieva, Mariana Marhova, Velizar Gochev and Vesselin Baev
Microorganisms 2025, 13(1), 160; https://doi.org/10.3390/microorganisms13010160 - 14 Jan 2025
Viewed by 265
Abstract
Lactobacillus is a key genus of probiotics commonly utilized for the treatment of oral infections The primary aim of our research was to investigate the probiotic potential of the newly isolated Levilactobacillus brevis DPL5 strain from human breast milk, focusing on its ability [...] Read more.
Lactobacillus is a key genus of probiotics commonly utilized for the treatment of oral infections The primary aim of our research was to investigate the probiotic potential of the newly isolated Levilactobacillus brevis DPL5 strain from human breast milk, focusing on its ability to combat biofilm-forming pathogens such as Staphylococcus aureus. Employing in vitro approaches, we demonstrate L. brevis DPL5′s ability to endure at pH 3 with survival rates above 30%, and withstand the osmotic stress often found during industrial processes like fermentation and freeze drying, retaining over 90% viability. The lyophilized cell-free supernatant of L. brevis DPL5 had a significant antagonistic effect against biofilm-producing nasal strains of Staphylococcus aureus, and it completely eradicated biofilms at subinhibitory concentrations of 20 mg·mL−1. Higher concentrations of 69 mg·mL−1 were found to have a 99% bactericidal effect, based on the conducted probability analysis, indicating the production of bactericidal bioactive extracellular compounds capable of disrupting the biofilm formation of pathogens like S. aureus. Furthermore, genome-wide sequencing and analysis of L. brevis DPL5 with cutting-edge Nanopore technology has uncovered over 50 genes linked to probiotic activity, supporting its ability to adapt and thrive in the harsh gut environment. The genome also contains multiple biosynthetic gene clusters such as lanthipeptide class IV, Type III polyketide synthase (T3PKS), and ribosomally synthesized, and post-translationally modified peptides (RiPP-like compounds), all of which are associated with antibacterial properties. Our study paves the way for the further exploration of DPL5, setting the stage for innovative, nature-inspired solutions to combat stubborn bacterial infections. Full article
(This article belongs to the Special Issue Beneficial Microorganisms and Antimicrobials: 2nd Edition)
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<p>Optical density (600 nm) after culturing in the presence of different sugars in a <span class="html-italic">Lactobacillus brevis</span> DPL5.</p>
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<p>Effect of different concentrations of sodium chloride on the growth of <span class="html-italic">Levilactobacillus brevis</span> DPL5.</p>
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<p>Survival of <span class="html-italic">L. brevis</span> DPL5 in different acidic conditions.</p>
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<p>Antagonistic activity against nasal <span class="html-italic">Staphylococcus aureus</span> strain: Plug diffusion test with 24 h (<b>A</b>), 48 h (<b>B</b>), and 72 h (<b>C</b>) <span class="html-italic">L. brevis</span> DPL5 cultivated on MRS agar. Agar well diffusion test showing the activity of <span class="html-italic">L. brevis</span> DPL5 cell-free supernatant (50 µL) after anaerobic cultivation in MRS broth for 24 h (<b>D</b>) and 48 h (<b>E</b>).</p>
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<p>Effect of different concentrations of lyophilized <span class="html-italic">L. brevis</span> DPL5 cell-free supernatant on the intensity of biofilm formation in nasal <span class="html-italic">S. aureus</span> strains.</p>
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<p>Confirmation of the <span class="html-italic">L. brevis</span> DPL5 via genome-to-genome comparisons in TYGS.</p>
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<p>Genomic maps of the clusters of the lanthipeptide class IV, T3PKS region, and RiPP-like region of the <span class="html-italic">L. brevis</span> DPL5.</p>
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16 pages, 1010 KiB  
Article
Lactic Acid Bacteria from Northern Thai (Lanna) Fermented Foods: A Promising Source of Probiotics with Applications in Synbiotic Formulation
by Nittiya Suwannasom, Achiraya Siriphap, Ornampai Japa, Chonthida Thephinlap, Chutamas Thepmalee and Krissana Khoothiam
Foods 2025, 14(2), 244; https://doi.org/10.3390/foods14020244 - 14 Jan 2025
Viewed by 347
Abstract
Northern Thai culture offers a rich variety of traditional fermented foods beneficial for gastrointestinal health. In this study, we characterized lactic acid bacteria (LAB) from various indigenous fermented foods as potential probiotic candidates and determined their properties for application in commercial synbiotic formulation. [...] Read more.
Northern Thai culture offers a rich variety of traditional fermented foods beneficial for gastrointestinal health. In this study, we characterized lactic acid bacteria (LAB) from various indigenous fermented foods as potential probiotic candidates and determined their properties for application in commercial synbiotic formulation. Five isolates demonstrating high tolerance to low pH (2.0) and 0.3% bile salts were collected and characterized. These included three strains of Lactiplantibacillus plantarum isolated from nham (NB1, NP2, and NP11) and two strains of Limosilactobacillus fermentum isolated from pla-som (PS4 and PS7). All the selected LAB isolates exhibited γ-hemolytic activity, strong antimicrobial activity, and high resistance to gastric and duodenal digestion conditions. Among the LAB isolates, L. plantarum NB1 demonstrated the highest capacity for adhesion to Caco-2 cells, auto-aggregation, and antioxidant activity, differing significantly (p < 0.05) from the other isolates. Furthermore, the NB1 strain exhibited preferential growth in the presence of commercial prebiotics (fructooligosaccharide, lactose, and inulin) and good survival after lyophilization, which is a desirable characteristic for a powdered ingredient. Therefore, the NB1 strain is a suitable probiotic candidate for applications in synbiotic formulation or as a functional food ingredient. Full article
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<p>Phylogenetic tree based on 16S rRNA gene sequencing analysis of the five LAB isolates and other closely related species.</p>
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<p>Survival of <span class="html-italic">L. plantarum</span> NB1, <span class="html-italic">L. plantarum</span> NP2, <span class="html-italic">L. plantarum</span> NP11, <span class="html-italic">L. fermentum</span> PS4, and <span class="html-italic">L. fermentum</span> PS7 under in vitro gastric and duodenal digestion conditions. <span class="html-italic">L. plantarum</span> TISTR 543 was used as the probiotic reference strain.</p>
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<p>Probiotic scores (<b>A</b>) and survivability after lyophilization (<b>B</b>) of <span class="html-italic">L. plantarum</span> NB1, <span class="html-italic">L. plantarum</span> NP2, <span class="html-italic">L. plantarum</span> NP11, <span class="html-italic">L. fermentum</span> PS4, and <span class="html-italic">L. fermentum</span> PS7. Statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between strains are indicated by different lowercase letters (a,b).</p>
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17 pages, 3067 KiB  
Article
Probiotic Characterization of Lactic Acid Bacteria from Donkey Feces in China
by Yanqiu Wu, Shousong Yue, Jinhui Yu, Fei Bian, Gao Chen and Yan Zhang
Animals 2025, 15(2), 207; https://doi.org/10.3390/ani15020207 - 14 Jan 2025
Viewed by 313
Abstract
Probiotics are beneficial to humans and animals and often used for regulating immunity, intestinal microbiota balance, and animal growth performance. Donkey husbandry has boomed in China in recent years and there is an urgent need for probiotics effective for improving donkey health. However, [...] Read more.
Probiotics are beneficial to humans and animals and often used for regulating immunity, intestinal microbiota balance, and animal growth performance. Donkey husbandry has boomed in China in recent years and there is an urgent need for probiotics effective for improving donkey health. However, studies on potential probiotic strains isolated from donkeys are scarce. This project aimed to screen LAB strains from donkey feces, detect their antimicrobial activity and evaluate their probiotic characteristics in vitro. Thirteen LAB isolates showed different degrees of antimicrobial activity against four indicator bacteria: three common pathogens (Escherichia coli, Staphylococcus aureus, and Salmonella typhimurium) and one pathogen restricted to equines (Salmonella. abortus equi), eight of which could inhibit all four pathogens. Seven isolates showed higher tolerance to low pH and bile salts, with >50% and >60% survival rates, respectively. Five of them had more than 50% survival rate to artificial gastric and intestinal fluids. Only three isolates possessed good properties, with >40% auto-aggregation, >40% hydrophobicity, and high co-aggregation with the indicator pathogens. An L9 isolate, identified as Ligilactobacillus salivarius, was sensitive to most antibiotics tested. Overall, these results indicate that the L. salivarius L9 isolate meets the requirements of the probiotics selection criteria in vitro and can potentially be developed as a probiotic for donkeys. Full article
(This article belongs to the Special Issue Current Research on Donkeys and Mules)
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<p>Growth and acid production rates of eight selected lactic acid bacteria strains isolated from donkey feces. Data are presented as the means of triplicate independent experiments. Error bars indicate standard deviations. OD<sub>600nm</sub> = optical density measured at 600 nm.</p>
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<p>Tolerance of lactic acid bacteria strains isolated from donkey feces to pH 2.0, 3.0, and 4.0. Data show the means of triplicate independent experiments, and error bars represent standard deviations. The same letter indicated at the same pH value represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) between isolates, whereas different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Tolerance of lactic acid bacteria strains isolated from donkey feces to 0.1, 0.2, and 0.3% bile salt concentrations. Data are shown as the means of triplicate independent experiments. Error bars represent standard deviations. The same letter at the same bile salt concentration represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) between isolates, whereas different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Tolerance of lactic acid bacteria strains isolated from donkey feces to artificial gastric and intestinal fluids. Data are shown as the means of triplicate independent experiments. Error bars represent standard deviations. The same letter in the same treatment represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) between isolates, whereas different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Auto-aggregation (<b>A</b>) and co-aggregation activities (<b>B</b>) of the lactic acid bacteria strains isolated from donkey feces. Data are shown as the means of triplicate independent experiments. Error bars indicate standard deviations. The same letter represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) and different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) between isolates that auto-aggregated and co-aggregated with the four indicator pathogens, respectively.</p>
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<p>Hydrophobicity of lactic acid bacteria strains isolated from donkey feces. Data are shown as the means of triplicate independent experiments. Error bars represent standard deviations. The same letter in the same hydrocarbon group represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) between isolates, whereas different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Phylogenetic tree based on the 16S rRNA gene sequence data of the selected L9 strain isolated from healthy donkey feces. The phylogenetic tree was constructed using the neighbor-joining method with 1000 bootstrap using MEGA 5.1.</p>
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<p>Effect on L9 isolate under different carbon source conditions. Data are shown as the means of triplicate independent experiments. Error bars represent standard deviations. The same letter represents non-significant differences (<span class="html-italic">p</span> &gt; 0.05) between carbon sources, whereas different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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16 pages, 15412 KiB  
Article
Effects of Dietary Supplementation with Lactobacillus reuteri Postbiotics on Growth Performance, Intestinal Flora Structure and Plasma Metabolome of Weaned Piglets
by Dongfeng Sun, Wenfei Tong, Shaochen Han, Mengjun Wu, Peng Li, Youguo Li and Yunxiang Liang
Animals 2025, 15(2), 204; https://doi.org/10.3390/ani15020204 - 14 Jan 2025
Viewed by 251
Abstract
Probiotics and their postbiotics have the potential to improve the health and growth performance of piglets, which has brought them widespread attention in the post-antibiotic era. In the present study, the effects of dietary supplementation of Lactobacillus reuteri postbiotics on the growth performance, [...] Read more.
Probiotics and their postbiotics have the potential to improve the health and growth performance of piglets, which has brought them widespread attention in the post-antibiotic era. In the present study, the effects of dietary supplementation of Lactobacillus reuteri postbiotics on the growth performance, intestinal flora structure and plasma metabolome of weaned piglets were investigated. A total of 816 healthy male piglets with uniform weight were divided into two treatment groups: piglets in the control (CTR) group were fed with a basic diet, and the ones in the LAC group were fed with the basic diet supplemented with 500 mg/kg Lactobacillus reuteri postbiotics. There were six replicates in each group and 68 piglets in each replicate. The animal trial lasted for 30 days. The feces and blood of piglets were collected for investigation, and the growth performance during the trial was counted. Our outcomes show that dietary supplementation with Lactobacillus reuteri postbiotics had no effect on the growth performance of piglets; however, it reduced the mortality rate of piglets by 6.37%. The levels of total superoxide dismutase in the serum, propionic acid and butyric acid in the feces were elevated, and the content of malondialdehyde in the serum was decreased with Lactobacillus reuteri postbiotics-treated piglets (p < 0.05). The fecal flora sequencing results show that the relative abundance of Firmicutes and monoglobus was upregulated, and the relative abundance of Bacteroides was downregulated with Lactobacillus reuteri postbiotics-treated piglets (p < 0.05). In addition, the levels of propionic acid and butyric acid in the feces were positively correlated with the relative abundance of Firmicutes and negatively correlated with the relative abundance of Bacteroides (p < 0.05). The plasma metabolome results show that dietary supplementation with Lactobacillus reuteri postbiotics raised the level of coenzyme Q10 in the serum, and the abundance of coenzyme Q10 was positively correlated with the relative abundance of Firmicutes and the level of total superoxide dismutase in the serum. In conclusion, dietary supplementation with Lactobacillus reuteri postbiotics contributed to improving the antioxidant function and reducing the mortality of piglets by regulating the structure of intestinal flora and upregulating the content of coenzyme Q10 in serum. Full article
(This article belongs to the Special Issue Feed Additives in Pig Feeding: 2nd Edition)
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<p>Effects of <span class="html-italic">Lactobacillus reuteri</span> postbiotics on growth performance, plasma antioxidant parameters and fecal short-chain fatty acid content of piglets. Among them, results of growth performance are arranged in (<b>A</b>), the antioxidant parameters in plasma are shown in (<b>B</b>), and levels of short-chain fatty acids are arranged in (<b>C</b>). FCR represents feed conversion efficiency, ADFI represents average daily feed intake, ADG represents average daily gain, MDA represents malondialdehyde, SOD represents superoxide dismutase, MPO represents myeloperoxidase, and GSH-px represents glutathione peroxidase. * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, and ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of <span class="html-italic">Lactobacillus reuteri</span> postbiotics on fecal flora structure of piglets. Among them, results of α-diversity are arranged in (<b>A</b>), the Venn and PCOA results are shown in (<b>B</b>) and (<b>C</b>), respectively. (<b>D</b>) and (<b>E</b>) show the relative abundance of the top ten bacteria at phyla level and genus level, respectively.</p>
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<p>Results of flora structure difference between CTR and LAC group based on LEFse and T-test analyses. Among them, results based on LEFse analysis are shown in (<b>A</b>,<b>B</b>), and outcomes based on T-test analysis are arranged in (<b>C</b>,<b>D</b>). * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, and ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Results of correlation analysis and functional prediction. Among them, results of correlation analysis are arranged in (<b>A</b>), and (<b>B</b>) shows outcomes of functional prediction. MDA represents malondialdehyde, SOD represents superoxide dismutase, MPO represents myeloperoxidase, GSH-px represents glutathione peroxidase. * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, and ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of Lactobacillus reuteri postbiotics on serum metabolome. Among them, results of PCA and OPLS-DA analyses are arranged in (<b>A</b>,<b>B</b>), the volcanic maps are shown in (<b>C</b>), and typical differential metabolite bars are arranged in (<b>D</b>). 1-hexadecanoyl-2-(9Z,12Z-octadecadienoyl)-sn-glycero-3-phosphocholine = MW0012968, 1-oleoyl-2-palmitoyl-sn-glycero-3-phosphocholine = MW0057016, coenzyme Q10 = MW0048971, PE-NMe2 (18:1(9Z)/18:1(9Z)) = MW0060366, and 1,2-distearoyl-sn-glycero-3-phosphocholine = MW0011927. 5-Hydroxy-6-methoxy-3-methyl-2-octaprenyl-1,4-benzoquinone = MW0142519, 3-hexanoyl-NBD cholesterol= MW0014037, glucocerebrosides = MW0053661, and 1-palmitoyl-3-adrenoyl-sn-glycerol = MW0049772, PC (14:1(9Z)/P-18:1(11Z)) = MW0056845. Red represents upregulation, and green represents downregulation.</p>
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<p>Analysis results of typical differential metabolites and their association with the indicators detected in the present study. Among them, VIP value diagram of differential metabolites and correlation chord diagram of metabolites are arranged in (<b>A</b>) and (<b>B</b>), respectively. Results of typical differential metabolites and their association with the indicators detected in the present study are shown in (<b>C</b>), and the results of peak area regarding coenzyme Q10 and 3-hexanoyl-NBD cholesterol are arranged in (<b>D</b>). MDA represents malondialdehyde, SOD represents superoxide dismutase, MPO represents myeloperoxidase, and GSH-px represents glutathione peroxidase. * 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05, ** 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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22 pages, 1764 KiB  
Article
Limosilactobacillus reuteri AJCR4: A Potential Probiotic in the Fight Against Oral Candida spp. Biofilms
by António Rajão, João P. N. Silva, Diana L. Almeida-Nunes, Paulo Rompante, Célia Fortuna Rodrigues and José Carlos Andrade
Int. J. Mol. Sci. 2025, 26(2), 638; https://doi.org/10.3390/ijms26020638 - 14 Jan 2025
Viewed by 410
Abstract
Oral candidiasis is one of the most common infections in the immunocompromised. Biofilms of Candida species can make treatments difficult, leading to oral infection recurrence. This research aimed to isolate a Lactobacillus with anti-Candida effects from the oral cavity. An oral Lactobacillus [...] Read more.
Oral candidiasis is one of the most common infections in the immunocompromised. Biofilms of Candida species can make treatments difficult, leading to oral infection recurrence. This research aimed to isolate a Lactobacillus with anti-Candida effects from the oral cavity. An oral Lactobacillus was isolated in caries-free individuals. The best isolate was evaluated against Candida spp. planktonic and biofilm forms. The bacterial impacts on Candida biofilms’ adhesion to acrylic discs were analyzed through an in vitro test. L. reuteri AJCR4 had the best anti-Candida activity in the preliminary screening. Results were promising in both planktonic and biofilms, particularly with C. albicans SC5314 and C. tropicalis ATCC750, where no viable cells were detected when using the cell-free supernatant (undiluted). In C. glabrata ATCC2001 and C. parapsilosis ATCC22019 biofilms, reductions of 3 Log10 and more than 2 Log10, respectively, were noted when using a cell suspension of L. reuteri ACJR4 (108 CFU/mL). On polymethyl methacrylate acrylic discs, the cell-free supernatant reduced Candida adhesion, resulting in no viable cell detection on the surface. In conclusion, L. reuteri AJCR4 demonstrated notable antifungal activity against Candida biofilms. This oral isolate and its postbiotic can be a potential alternative strategy to oral candidiasis, especially to treat recalcitrant infections. Full article
(This article belongs to the Special Issue Oral Microbiome and Oral Diseases: 2nd Edition)
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<p><span class="html-italic">C. albicans</span> SC5314 (<b>a</b>), <span class="html-italic">C. tropicalis</span> ATCC750 (<b>b</b>), <span class="html-italic">C. parapsilosis</span> ATCC 22019 (<b>c</b>), and <span class="html-italic">C. glabrata</span> ATCC2001 (<b>d</b>). CFUs after being co-cultured with <span class="html-italic">L. reuteri</span> and <span class="html-italic">L. rhamnosus</span> oral isolates. Mean, error bars indicate standard deviations.</p>
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<p>Cellular viability of planktonic cells of (<b>a</b>) <span class="html-italic">C. albicans</span> SC5314, (<b>b</b>) <span class="html-italic">C. tropicalis</span> ATCC750, (<b>c</b>) <span class="html-italic">C. glabrata</span> ATCC2001, and (<b>d</b>) <span class="html-italic">C. parapsilosis</span> ATCC 22019, treated with different concentrations of <span class="html-italic">L. reuteri</span> AJCR4. Bars represent the mean and standard deviation. (* <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).</p>
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<p>Biofilms cell reduction in (<b>a</b>) <span class="html-italic">C. albicans</span> SC5314, (<b>b</b>) <span class="html-italic">C. tropicalis</span> ATCC750, (<b>c</b>) <span class="html-italic">C. parapsilosis</span> ATCC 22019, and (<b>d</b>) <span class="html-italic">C. glabrata</span> ATCC2001 when treated with different concentrations of <span class="html-italic">L. reuteri</span> AJCR4 (10<sup>8</sup>, 10<sup>7</sup>, and 10<sup>6</sup> CFU/mL) undiluted (CFS) and diluted 10 and 100 times (CFS 1:10 and CFS 1:100, respectively). Bars represent the mean and standard deviation (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Biofilm reduction in (<b>a</b>) <span class="html-italic">C. albicans</span> H37, (<b>b</b>) <span class="html-italic">C. albicans</span> H43, (<b>c</b>) <span class="html-italic">C. albicans</span> MYK2760, (<b>d</b>) <span class="html-italic">C. tropicalis</span> C7, (<b>e</b>) <span class="html-italic">C. glabrata</span> H49, and (<b>f</b>) <span class="html-italic">C. glabrata</span> 15 when treated with different concentrations of <span class="html-italic">L. reuteri</span> AJCR4 (10<sup>8</sup>, 10<sup>7</sup>, and 10<sup>6</sup> CFU/mL) and cells-free supernatant, undiluted CFS, and CFS diluted 10 and 100 times (CFS 1:10 and CFS 1:100, respectively). Bars represent the mean and standard deviation (** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Viable biofilm cells of <span class="html-italic">C. albicans</span> SC5314, <span class="html-italic">C. tropicalis</span> ATCC750, <span class="html-italic">C. parapsilosis</span> ATCC 22019, and <span class="html-italic">C. glabrata</span> ATCC2001. The biofilms were formed on the acrylic discs treated with <span class="html-italic">L. reuteri</span> AJCR4 supernatant and their corresponding controls with MRS broth medium. <b>C</b> (control group) and <b>T</b> (treatment) of each <span class="html-italic">Candida</span> spp. with <span class="html-italic">L. reuteri</span> AJCR4 CFS. Bars represent the mean and standard deviation (**** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Percentage of co-aggregation among each <span class="html-italic">Candida</span> reference strain with <span class="html-italic">L. reuteri</span> AJCR4, at three time points (1, 2, and 4 h). Bars represent the mean and standard deviation (**** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Antifungal activity of the cell-free supernatant both untreated (CFS) and with neutralization of pH (CFS pH7), heat treatment to 100 °C (CFS 100 °C), and enzyme treatments (CFS peroxidase; CFS proteinase) in (<b>a</b>) <span class="html-italic">C. albicans</span> SC5314, (<b>b</b>) <span class="html-italic">C. tropicalis</span> ATCC750, (<b>c</b>) <span class="html-italic">C. parapsilosis</span> ATCC 22019, and (<b>d</b>) <span class="html-italic">C. glabrata</span> ATCC2001. Bars represent the mean and standard deviation (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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19 pages, 1054 KiB  
Review
Impact of Microbiota on Irritable Bowel Syndrome Pathogenesis and Management: A Narrative Review
by Mhd Bashir Almonajjed, Mahdi Wardeh, Abdallah Atlagh, Abdulrahman Ismaiel, Stefan-Lucian Popa, Flaviu Rusu and Dan L. Dumitrascu
Medicina 2025, 61(1), 109; https://doi.org/10.3390/medicina61010109 - 13 Jan 2025
Viewed by 453
Abstract
Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder, affecting 3–5% of the global population and significantly impacting patients’ quality of life and healthcare resources. Alongside physical symptoms such as abdominal pain and altered bowel habits, many individuals experience psychological comorbidities, including anxiety [...] Read more.
Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder, affecting 3–5% of the global population and significantly impacting patients’ quality of life and healthcare resources. Alongside physical symptoms such as abdominal pain and altered bowel habits, many individuals experience psychological comorbidities, including anxiety and depression. Recent research has highlighted the critical role of the gut microbiota in IBS, with dysbiosis, characterized by an imbalance in microbial diversity, frequently observed in patients. The gut–brain axis, a bidirectional communication network between the gut and central nervous system, plays a central role in the development of IBS symptoms. Although interventions such as probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT) have demonstrated potential in modulating the gut microbiota and alleviating symptoms, their efficacy remains an area of ongoing investigation. This review examines the interactions between the gut microbiota, immune system, and brain, emphasizing the need for personalized therapeutic strategies. Future research should aim to identify reliable microbiota-based biomarkers for IBS and refine microbiome-targeted therapies to enhance patient outcomes. Full article
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<p>Microbiota alterations in IBS. (The figure is adapted with modifications from Surdea-Blaga et al., 2024, Microbiome in irritable bowel syndrome: advances in the field—A scoping review [<a href="#B25-medicina-61-00109" class="html-bibr">25</a>]).</p>
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<p>An overview of suggested factors involved in IBS.</p>
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<p>Summary of the mechanisms of action of different therapeutic approaches for IBS.</p>
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26 pages, 18627 KiB  
Article
Olivetol’s Effects on Metabolic State and Gut Microbiota Functionality in Mouse Models of Alimentary Obesity, Diabetes Mellitus Type 1 and 2, and Hypercholesterolemia
by Anastasia A. Zabolotneva, Katerina E. Popruga, Valentin V. Makarov, Sergei M. Yudin, Andrei M. Gaponov, Sergei A. Roumiantsev and Aleksandr V. Shestopalov
Biomedicines 2025, 13(1), 183; https://doi.org/10.3390/biomedicines13010183 - 13 Jan 2025
Viewed by 301
Abstract
Background: Disorders of glucose and lipid metabolism, such as obesity, diabetes mellitus, or hypercholesterolemia, can cause serious complications, reduce quality of life, and lead to increased premature mortality. Olivetol, a natural compound, could be proposed as a promising therapeutic agent for preventing, treating, [...] Read more.
Background: Disorders of glucose and lipid metabolism, such as obesity, diabetes mellitus, or hypercholesterolemia, can cause serious complications, reduce quality of life, and lead to increased premature mortality. Olivetol, a natural compound, could be proposed as a promising therapeutic agent for preventing, treating, or alleviating metabolic complications of such pathological conditions. Methods: In this study, the researchers conducted a broad parallel investigation of olivetol’s effects on metabolic state and gut microbiota functionality in mouse models of alimentary obesity, diabetes mellitus type 1 and 2, and hypercholesterolemia. Results: According to the results of the study, olivetol caused a lowering of body weight in C57Bl6 mice fed a high-fat diet and in ldlr(−/−) mice, decreased serum glucose levels in db/db mice, improved lipid metabolism in ldlr(−/−) mice, and prevented inflammatory infiltration of the pancreas and loss of insulin secretion in NOD mice. In addition, olivetol affected the composition and functional activity of gut microbiota communities, inducing an expansion of probiotic species such as Akkermansia muciniphila and Bacteroides acidifaciens and depleting the representation of pathobionts such as Prevotella, although olivetol supplementation did not influence the diversity or richness of the communities. Conclusions: These results suggest that olivetol is a promising therapeutic agent for preventing, treating, or alleviating the metabolic complications of obesity, diabetes mellitus type 1 and 2, and hypercholesterolemia; however, more investigations are required in order to attain a full understanding of its physiological effects. Full article
(This article belongs to the Special Issue Recent Advances in Obesity-Related Metabolic Diseases)
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<p>Experimental design. Four mouse models were selected to investigate the effects of olivetol on the metabolic state and gut microbiota composition of mice fed a standard diet (SD) or a high-fat diet (HFD). Each group, including control groups that did not receive olivetol, consisted of 10 mice. n indicates the total number of mice taken for the individual model studied in the experiment. HFD-fed C57Bl6 mice and <span class="html-italic">db</span>/<span class="html-italic">db</span> mice developed an obese phenotype compared with SD-fed C57Bl6 mice.</p>
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<p>Mouse body weight dynamics during 90 days of the experiment: (<b>a</b>) C57Bl6 mice weights; (<b>b</b>) ANOVA followed by unpaired <span class="html-italic">t</span>-tests of C57Bl6 mice BW after 90 days of the experiment (** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001); (<b>c</b>) <span class="html-italic">db</span>/<span class="html-italic">db</span> mice weights; (<b>d</b>) unpaired <span class="html-italic">t</span>-test of <span class="html-italic">db</span>/<span class="html-italic">db</span> mice’s BW after 90 days of the experiment (no significant differences); (<b>e</b>) NOD mice weights; (<b>f</b>) unpaired <span class="html-italic">t</span>-test of NOD mice BW after 90 days of the experiment (no significant differences); (<b>g</b>) <span class="html-italic">ldlr</span>(−/−) mice weights; (<b>h</b>) unpaired <span class="html-italic">t</span>-test of <span class="html-italic">ldlr</span>(−/−) mice BW after 90 days of the experiment (** <span class="html-italic">p</span> &lt; 0.01). BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Results of the serum glucose dynamics during 90 days of experiment. (<b>a</b>–<b>d</b>), TAG (<b>e</b>), and cholesterol (<b>f</b>) levels were measured in the different experimental mice groups. Comparisons were carried out using unpaired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001). BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Results of the serum insulin (<b>a</b>), adiponectin (<b>b</b>), leptin (<b>c</b>), and myostatin (<b>d</b>) levels measured in the different experimental mice groups. Comparisons were carried out using unpaired <span class="html-italic">t</span>-tests (* <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001). BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Results of unpaired <span class="html-italic">t</span>-tests for alpha diversity indexes ((<b>a</b>) for PD whole tree, (<b>b</b>) for Observed OTUs, and (<b>c</b>) for Shannon), indicating changes in the richness and diversity of the mouse colon microbiota. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001. BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Mouse gut microbiota composition at phylum level in different groups of mice: (<b>a</b>) for C57Bl6 mice (BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation); (<b>b</b>) for NOD mice (NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation); (<b>c</b>) for <span class="html-italic">db</span>/<span class="html-italic">db</span> mice (DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation); (<b>d</b>) for <span class="html-italic">ldlr</span>(−/−) mice (LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation).</p>
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<p>Effects of olivetol supplementation on the composition of the mouse gut microbiota community at phylum and class levels: (<b>a</b>) <span class="html-italic">Verrucomicrobia</span>; (<b>b</b>) Bacteroidetes; (<b>c</b>) <span class="html-italic">Proteobacteria</span>; (<b>d</b>) <span class="html-italic">Actinobacteria</span>; (<b>e</b>) <span class="html-italic">Betaproteobacteria</span>; (<b>f</b>) <span class="html-italic">Coriobacteriia</span>; (<b>g</b>) <span class="html-italic">Erysipelotrichi</span>; (<b>h</b>) <span class="html-italic">Verrucomicrobiae</span>. Comparisons were made via unpaired <span class="html-italic">t</span>-tests. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Effects of olivetol supplementation on the composition of the mouse gut microbiota community at the order level: (<b>a</b>) <span class="html-italic">Erysipelotrichales</span>; (<b>b</b>) <span class="html-italic">Coriobacteriales</span>; (<b>c</b>) <span class="html-italic">Verrucomicrobiales</span>; (<b>d</b>) <span class="html-italic">Burkhoderiales</span>; (<b>e</b>) <span class="html-italic">Bifidobacteriales</span>. Comparisons were made via unpaired <span class="html-italic">t</span>-tests. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Effects of olivetol supplementation on the composition of the mouse gut microbiota community at genus and species level: (<b>a</b>) <span class="html-italic">Akkermansia</span>; (<b>b</b>) <span class="html-italic">Allobaculum</span>; (<b>c</b>) <span class="html-italic">Parabacteroides</span>; (<b>d</b>) <span class="html-italic">Prevotella</span>; (<b>e</b>) <span class="html-italic">Bifidobacterium</span>; (<b>f</b>) <span class="html-italic">Bacteroides</span>; (<b>g</b>) <span class="html-italic">Adlercreutzia</span>; (<b>h</b>) <span class="html-italic">Bacteroides acidifaciens</span>; (<b>i</b>) <span class="html-italic">Bifidobacterium pseudolongum</span>; (<b>j</b>) <span class="html-italic">Akkermansia muciniphila.</span> Comparisons were made via unpaired <span class="html-italic">t</span>-tests. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. BL/SD, C57Bl6 mice were fed a standard diet; BL/HFD, C57Bl6 mice were fed a high-fat diet; BL/SD + C5, C57Bl6 mice were fed a standard diet with olivetol supplementation; BL/HFD + C5, C57Bl6 mice were fed a high-fat diet with olivetol supplementation; LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet; LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice were fed a standard diet with olivetol supplementation; NOD/SD, NOD mice were fed a standard diet; NOD/SD + C5, NOD mice were fed a standard diet with olivetol supplementation; DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet; and DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice were fed a standard diet with olivetol supplementation.</p>
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<p>Tissue sections of mice receiving a diet without or with (+C5) olivetol supplementation (magnification ×200). Columns: (<b>A</b>) liver; (<b>B</b>) pancreas; (<b>C</b>) skeletal muscle; (<b>D</b>) adipose tissue. Rows: 1—BL/SD, C57Bl6 mice fed a standard diet; 2—BL/SD + C5, C57Bl6 mice fed a standard diet with olivetol supplementation; 3—BL/HFD, C57Bl6 mice fed a high-fat diet; 4—BL/HFD + C5, C57Bl6 mice fed a high-fat diet with olivetol supplementation; 5—NOD/SD, NOD mice fed a standard diet (beta cells were stained by antibodies to insulin); 6—NOD/SD + C5, NOD mice fed a standard diet with olivetol supplementation (beta cells were stained by antibodies to insulin); 7—DB/SD, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice fed a standard diet; 8—DB/SD + C5, <span class="html-italic">db</span>/<span class="html-italic">db</span> mice fed a standard diet with olivetol supplementation; 9—LDLR/SD, <span class="html-italic">ldlr</span>(−/−) mice fed a standard diet; 10—LDLR/SD + C5, <span class="html-italic">ldlr</span>(−/−) mice fed a standard diet with olivetol supplementation. Tissue sections for all mice in groups can be provided by the corresponding author under request.</p>
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12 pages, 2046 KiB  
Article
Evidence for the Worldwide Distribution of a Bile Salt Hydrolase Gene in Enterococcus faecium Through Horizontal Gene Transfer
by Hiroyuki Kusada and Hideyuki Tamaki
Int. J. Mol. Sci. 2025, 26(2), 612; https://doi.org/10.3390/ijms26020612 - 13 Jan 2025
Viewed by 255
Abstract
Bile salt hydrolase (BSH), a probiotic-related enzyme with cholesterol-assimilating and anti-hypercholesterolemic abilities, has been isolated from intestinal bacteria; however, BSH activity of bacteria in bile-salt-free (non-intestinal) environments is largely unknown. Here, we aimed to identify BSH from non-intestinal Enterococcus faecium and characterize its [...] Read more.
Bile salt hydrolase (BSH), a probiotic-related enzyme with cholesterol-assimilating and anti-hypercholesterolemic abilities, has been isolated from intestinal bacteria; however, BSH activity of bacteria in bile-salt-free (non-intestinal) environments is largely unknown. Here, we aimed to identify BSH from non-intestinal Enterococcus faecium and characterize its enzymatic function. We successfully isolated a plasmid-encoded bsh (efpBSH) from E. faecium, and the recombinant EfpBSH showed BSH activity that preferentially hydrolyzed taurine-conjugated bile salts, unlike the activity of known BSHs. EfpBSH functioned optimally at pH 4.0 and 50 °C. EfpBSH exhibited very low amino acid sequence similarity (48.46%) to EfBSH from E. faecalis T2 isolated from human urine, although 241 sequences with 100% identity to EfpBSH were found in both plasmids and chromosomes of E. faecium strains inhabiting intestinal and non-intestinal environments. Phylogenetically, EfpBSH was not affiliated with any known BSH phylogroup and was clearly distinguished from previously identified BSHs from intestinal lactic acid bacteria. Our genome database analysis demonstrated that horizontal gene transfer causes global efpBSH distribution among E. faecium strains in various environments (soil, water, and intestinal samples) and geographical regions (Asia, Africa, Europe, North America, South America, and Australia/Oceania). Overall, our findings are the first to indicate that BSH is not an intestine-specific enzyme and that hitherto-overlooked probiotic candidates with BSH activity can exist in diverse environments. Full article
(This article belongs to the Special Issue Molecular Research on Bacteria)
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<p>SDS-PAGE of purified EfpBSH. Lane M; 3-Color Prestained XL-Ladder (APRO Science, Tokushima, Japan).</p>
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<p>Enzymatic characterization of EfpBSH. (<b>A</b>) BSH activity and substrate specificity of EfpBSH. The tested substrates were glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), and taurodeoxycholic acid (TDCA). Each bile salt solution was mixed with buffer instead of EfpBSH (white bars) for specific negative controls. Values are indicated as means of five technical replicates (<span class="html-italic">n</span> = 5). Error bars represent standard deviation (SD). Identification of the effects of temperature (<b>B</b>) and pH (<b>C</b>) on BSH activity. The tested temperature and pH ranges were 20–80 °C and pH 3.0–10.0, respectively. Each value is expressed as the mean of three technical replicates (<span class="html-italic">n</span> = 3). Maximum activity was taken as 100%. Error bars indicate SD. For all analyses, a <span class="html-italic">p</span>-value less than 0.05 (* <span class="html-italic">p</span> &lt; 0.05) was defined as statistically significant.</p>
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<p>Multiple alignment analysis of EfpBSH. The amino acid sequence of EfpBSH was compared with BSHs from <span class="html-italic">Enterococcus faecalis</span>, <span class="html-italic">Listeria monocytogenes</span>, and <span class="html-italic">Enterococcus faecium</span>. The black and gray backgrounds indicate identical and similar amino acid residues, respectively. The conserved amino acid residues (Cys, Arg, Asp, Asn, and Arg) associated with the catalytic active site are boxed in red lines. Sources of BSHs: EfBSH (4WL3) from <span class="html-italic">E. faecalis</span> T2 [<a href="#B23-ijms-26-00612" class="html-bibr">23</a>]; LmBSH (QHF62338) from <span class="html-italic">L. monocytogenes</span> EGD-e [<a href="#B24-ijms-26-00612" class="html-bibr">24</a>]; and BSH from <span class="html-italic">E. faecium</span> LR2 [<a href="#B14-ijms-26-00612" class="html-bibr">14</a>].</p>
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<p>Phylogenetic analysis of EfpBSH. The phylogenetic tree was constructed using MEGA X software version 10.1.8 based on the neighbor-joining method (1000 bootstrap replications). Bootstrap values are represented by circles, whose sizes correlate with the bootstrap values. Each enzyme name was defined based on the names of genus, species, and strain (<a href="#ijms-26-00612-t001" class="html-table">Table 1</a>). EfpBSH was indicated in red. Each BSH group was highlighted by different background color. CpBSH, BSH from <span class="html-italic">Clostridium perfringens</span> 13, was used as the outgroup.</p>
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<p>Comparative genomic analysis of <span class="html-italic">efpBSH</span>-encoding plasmids and chromosomes in <span class="html-italic">E. faecium</span> strains. A physical map and gene organization of <span class="html-italic">efpBSH</span> and their surrounding genes are provided (reversed organization in plasmid B and chromosome E and F). Each sequence’s information is listed in the table. Light pink shadings indicate homologous genes with &gt;95% amino acid sequence similarity. The ORFs are color-coded according to predicted protein functions provided in the middle panel. The scale bar indicates 1000 bp.</p>
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25 pages, 2359 KiB  
Review
Malnutrition and Its Influence on Gut sIgA–Microbiota Dynamics
by Monica Profir, Robert Mihai Enache, Oana Alexandra Roşu, Luciana Alexandra Pavelescu, Sanda Maria Creţoiu and Bogdan Severus Gaspar
Biomedicines 2025, 13(1), 179; https://doi.org/10.3390/biomedicines13010179 - 13 Jan 2025
Viewed by 354
Abstract
In the current era, malnutrition is seen as both undernutrition and overweight and obesity; both conditions are caused by nutrient deficiency or excess and improper use or imbalance in the intake of macro and micronutrients. Recent evidence suggests that malnutrition alters the intestinal [...] Read more.
In the current era, malnutrition is seen as both undernutrition and overweight and obesity; both conditions are caused by nutrient deficiency or excess and improper use or imbalance in the intake of macro and micronutrients. Recent evidence suggests that malnutrition alters the intestinal microbiota, known as dysbiosis. Secretory immunoglobulin A (sIgA) plays an important role in maintaining and increasing beneficial intestinal microbiota populations and protecting against pathogenic species. Depletion of beneficial bacterial populations throughout life is also conditioned by malnutrition. This review aims to synthesize the evidence that establishes an interrelationship between diet, malnutrition, changes in the intestinal flora, and sIgA levels. Targeted nutritional therapies combined with prebiotic, probiotic, and postbiotic administration can restore the immune response in the intestine and the host’s homeostasis. Full article
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<p>Categories of malnutrition. Undernutrition: includes wasting (low weight-for-height), stunting (low height-for-age), and underweight (low weight-for-age). Micronutrient deficiencies: refers to a lack of essential vitamins and minerals. Overnutrition: encompasses overweight, obesity, and diet-related noncommunicable diseases. Created using BioRender.com (accessed on 22 October 2024).</p>
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<p>The most important factors that influence the gut microbiota composition in humans. Created using BioRender.com (accessed on 30 November 2024).</p>
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<p>Undernutrition predominantly affects two vulnerable groups: newborns and the elderly, as well as patients with chronic diseases. This condition often results in dysbiosis, characterized by malabsorption and chronic inflammation, significantly increasing the risk of immune dysfunction and other health complications. Created using BioRender.com (accessed on 30 November 2024).</p>
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<p>The unique role of sIgA in maintaining gut homeostasis. It helps regulate microbial communities and fortifies the mucosal barrier by directly interacting with gut microbes. sIgA binds to potentially harmful pathogens, preventing their adhesion to the gut lining while promoting the presence of beneficial microbial species. Dysregulation of sIgA can disrupt this balance, leading to microbial imbalances where harmful bacteria dominate, contributing to or exacerbating disease states. Created using BioRender.com (accessed on 30 November 2024).</p>
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<p>Dietary interventions and nutritional supplements enhance fecal sIgA levels, promoting a diverse and healthy gut microbiota. Diets rich in fiber and fermented foods like yogurt and kimchi contribute to this effect. One of the mechanisms involved is the production of SCFAs, which possess anti-inflammatory properties and help maintain gut homeostasis. Created using BioRender.com (accessed on 30 November 2024).</p>
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