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18 pages, 4115 KiB  
Article
Novel Co-Cultivation Bioprocess with Immobilized Paenibacillus polymyxa and Scenedesmus obliquus for Lipid and Butanediol Production
by Jnanada Shrikant Joshi, Laura Fladung, Olaf Kruse and Anant Patel
Microorganisms 2025, 13(3), 606; https://doi.org/10.3390/microorganisms13030606 - 5 Mar 2025
Viewed by 358
Abstract
Microalgal biotechnology is gaining attention due to its potential to produce pigments, lipids, biofuels, and value-added products. However, challenges persist in terms of the economic viability of microalgal lipid production in photobioreactors due to slow growth rates, expensive media, complex downstream processing, limited [...] Read more.
Microalgal biotechnology is gaining attention due to its potential to produce pigments, lipids, biofuels, and value-added products. However, challenges persist in terms of the economic viability of microalgal lipid production in photobioreactors due to slow growth rates, expensive media, complex downstream processing, limited product yields, and contamination risks. Recent studies suggest that co-cultivating microalgae with bacteria can enhance the profitability of microalgal bioprocesses. Immobilizing bacteria offers advantages such as protection against shear forces, the prevention of overgrowth, and continuous product secretion. Previous work has shown that biopolymeric immobilization of Paenibacillus polymyxa enhances 2,3-butanediol production. In this study, a novel co-fermentation process was developed by exploiting the chemical crosstalk between a freshwater microalga Scenedesmus obliquus, also known as Tetradesmus obliquus, and an immobilized plant-growth-promoting bacterium, Paenibacillus polymyxa. This co-cultivation resulted in increased metabolite production, with a 1.5-fold increase in the bacterial 2,3-butanediol concentration and a 3-fold increase in the microalgal growth rates compared to these values in free-cell co-cultivation. Moreover, the co-culture with the immobilized bacterium exhibited a 5-fold increase in the photosynthetic pigments and a 3-fold increase in the microalgal lipid concentration compared to these values in free-cell co-cultivation. A fixed bed photobioreactor was further constructed, and the co-cultivation bioprocess was implemented to improve the bacterial 2,3-butanediol and microalgal lipid production. In conclusion, this study provides conclusive evidence for the potential of co-cultivation and biopolymeric immobilization techniques to enhance 2,3-butanediol and lipid production. Full article
(This article belongs to the Special Issue The Application Potential of Microalgae in Green Biotechnology)
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<p>Setup of the utilized self-constructed fixed bed photobioreactor (<b>A</b>) and a photo of the utilized self-constructed tubular fixed bed photobioreactor with the main light source of the tubular photobioreactor removed to visualize the medium (<b>B</b>). Fluidized bed photobioreactor can improve the product yields with proper mixing due to gas sparging and improved light transmission (<b>C</b>).</p>
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<p>Maximum specific growth rates (μmax) determined in the cultivation of axenic microalgae in the PS medium with the addition of empty chitosan-coated carrageenan beads and the co-cultivation with immobilized bacteria. <span class="html-italic">n</span> = 5; mean ± SD. Different letters a, b, and c indicate a significant difference according to the one-way ANOVA F<sub>2,14</sub> = 1501.51; <span class="html-italic">p</span> &lt; 0.001 with Bonferroni’s post hoc test at <span class="html-italic">p</span> &lt; 0.05. <span class="html-italic">n</span> = 5; mean <math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math> SD; one-way ANOVA with Bonferroni’s post hoc test, <span class="html-italic">p</span> &lt; 0.05 (<b>A</b>). Total chlorophyll contents determined in the cultivation of axenic microalgae in the PS medium with the addition of empty chitosan-coated carrageenan beads and the co-cultivation with immobilized bacteria. <span class="html-italic">n</span> = 5; mean ± SD. Different letters a, b, and c indicate a significant difference according to the one-way ANOVA F<sub>2,14</sub> = 2894.11; <span class="html-italic">p</span> &lt; 0.001 with Bonferroni’s post hoc test at <span class="html-italic">p</span> &lt; 0.05 (<b>B</b>).</p>
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<p>Determined 2,3-BDL concentrations of co-cultivation of free cells and chitosan-coated calcium alginate bead- and chitosan-coated carrageenan bead-immobilized bacteria. <span class="html-italic">n</span> = 5; mean ± SD. Different letters a and b indicate a significant difference according to one-way ANOVA F<sub>2,24</sub> = 166.58; <span class="html-italic">p</span> &lt; 0.001 with Bonferroni’s post hoc test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Co-cultivation of axenic <span class="html-italic">S. obliquus</span> with <span class="html-italic">P. polymyxa</span> immobilized in chitosan-coated carrageenan beads in PS medium using a self-constructed tubular fixed bed photobioreactor over 366 h; photos with the main light source of the tubular photobioreactor removed to visualize the medium.</p>
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<p>Samples of <span class="html-italic">S. obliquus</span> in co-culture with chitosan-coated carrageenan <span class="html-italic">P. polymyxa</span> beads in PS medium in a photobioreactor for 0 h to 336 h.</p>
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20 pages, 1459 KiB  
Article
Quantitative Differences in the Human Intestinal Microbiota Through the Stages of Life: Infants, Children, Adults and the Elderly
by Jelena Štšepetova, Natalja Šebunova, Tiia Voor, Hiie Soeorg, Merle Rätsep, Reet Mändar, Marika Mikelsaar and Epp Sepp
Microbiol. Res. 2025, 16(3), 60; https://doi.org/10.3390/microbiolres16030060 - 5 Mar 2025
Viewed by 179
Abstract
The aim of this study was to compare the human intestinal microbiota in different age groups, elucidating the precise stages of life in which the gut microbiota evolves its specific characteristics in terms of composition and diversity, as well as associating different bacterial [...] Read more.
The aim of this study was to compare the human intestinal microbiota in different age groups, elucidating the precise stages of life in which the gut microbiota evolves its specific characteristics in terms of composition and diversity, as well as associating different bacterial groups for prediction of their intertwined metabolic role, considering their importance in human health. The quantitative composition, Bacteroidetes/Firmicutes (B/F) ratio, counts and diversity indices of faecal samples obtained from infant, child, adult and elderly individuals were assessed via quantitative real-time polymerase chain reaction (qPCR). The intestinal microbiota of infants expressed the highest B/F ratio and diversity. The total bacteria counts, Bacteroides-Prevotella and Blautia coccoides-Eubacterium rectale groups were the most abundant in adults and infants, while child and elderly individuals presented the highest counts of Firmicutes and Lactobacillus sp. In infants, the counts of Enterococcus sp., Streptococcus sp., Enterobacteriaceae, Veillonella sp. and Clostridium perfringens groups were higher, when compared to the other age groups. The tightest positive correlations between bacteria within age groups were found for the B. coccoides-E. rectale, C. leptum (incl. Faecalibacterium prausnitzii), Bacteroidetes-Prevotella and Atopobium groups. Through the stages of life, the quantitative composition and diversity of intestinal microbiota evolves with two changing maximal peaks of predominant groups, with bacterial diversity decreasing from infant to child stage, showing unitary stabilization in adults and presenting a wide individual range in the elderly. The high counts of Bacteroidetes and Clostridium from the phylum Firmicutes, present throughout all life stages, mainly influence the composition and metabolic activity of other bacteria. Recognizing age-specific differences may provide a basis for comparing different geographic populations and predicting the intertwined metabolites of various bacteria, which have certain implications for health. Full article
28 pages, 1085 KiB  
Review
Microbial Influences on Amyotrophic Lateral Sclerosis: The Gut–Brain Axis and Therapeutic Potential of Microbiota Modulation
by Victòria Ayala, Laia Fontdevila, Santiago Rico-Rios, Mònica Povedano, Pol Andrés-Benito, Pascual Torres, José C. E. Serrano, Reinald Pamplona and Manuel Portero-Otin
Sclerosis 2025, 3(1), 8; https://doi.org/10.3390/sclerosis3010008 - 5 Mar 2025
Viewed by 135
Abstract
Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by the progressive degeneration of motor neurons. The gut microbiota, a community of microorganisms in the digestive tract, has recently been implicated in ALS pathogenesis through its influence on neuroinflammation and metabolic pathways. [...] Read more.
Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by the progressive degeneration of motor neurons. The gut microbiota, a community of microorganisms in the digestive tract, has recently been implicated in ALS pathogenesis through its influence on neuroinflammation and metabolic pathways. This review explores the potential role of digestive microbiota and its metabolites in ALS progression and investigates therapeutic approaches targeting gut microbiota. Methods: A comprehensive review of the current literature was conducted to assess the relationship between gut microbiota composition, microbial metabolites, and ALS progression in patients. We searched for published reports on microbiota composition, microbial metabolites, and ALS, emphasizing the complex interplay between dysbiosis, neuroinflammation, and systemic metabolism. Special emphasis was placed on studies exploring short-chain fatty acids (SCFAs), bacterial amyloids (curli-like factors), and neurotoxins such as β-methylamino-L-alanine (BMAA). The role of the liver–gut axis was evaluated as well. The potential changes in microbiota would sustain the rationale for therapeutic strategies such as probiotics, prebiotics, fecal microbiota transplantation (FMT), and dietary interventions. Results: ALS patients exhibit gut dysbiosis, characterized by reduced SCFA-producing bacteria and an increase in potentially pathogenic genera. Of note, different studies do not agree on common patterns of microbiota being linked to ALS, supporting the need for further, more extensive studies. Dysbiosis sometimes correlates with systemic inflammation and disrupted liver function, amplifying neuroinflammatory responses. Key microbial metabolites, including SCFAs, bacterial amyloids, and BMAA, may exacerbate motor neuron degeneration by promoting protein misfolding, oxidative stress, and neuroinflammation. Emerging therapeutic strategies, including probiotics and FMT, show potential in restoring microbial balance, although clinical data in ALS patients remain limited. Conclusions: The gut microbiota could modulate neuroinflammation and systemic metabolism in ALS. Microbiota-targeted therapies, such as probiotics and dietary interventions, represent promising avenues for mitigating disease progression. Further research is required to validate these interventions through large-scale, longitudinal studies and to develop personalized microbiota-based treatments tailored to individual ALS phenotypes. Full article
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<p>Flowchart of the selection of reviewed studies.</p>
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<p>Potential gut contribution to ALS pathophysiology via the gut–liver–brain axis. Gut dysbiosis in ALS patients, characterized by a decrease in short-chain fatty acid (SCFA)-producing bacteria and an increase in potentially pathogenic bacteria, may contribute to neuroinflammation and disease progression. Microbial metabolites, including SCFAs (such as butyrate, with neuroprotective properties), lipopolysaccharides (LPS, with pro-inflammatory effects), and toxins (such as β-methylamino-L-alanine (BMAA) or D-glutamate, with neurotoxic potential), influence the central nervous system (CNS) through the gut–liver–brain axis. Anatomically connected to the intestine, the liver plays a crucial role in modulating the systemic inflammatory response. Liver dysfunction, common in ALS, can exacerbate neuroinflammation by allowing endotoxins, such as LPS, and other bacterial metabolites to enter the systemic circulation, activating immune cells in the CNS (microglia and astrocytes) and promoting the release of proinflammatory cytokines. Furthermore, alterations in bile acid production and metabolism, modulated by microbiota, can affect signaling through receptors such as FXR and TGR5, influencing inflammation and lipid metabolism. The interplay between gut dysbiosis, liver dysfunction, and neuroinflammation establishes a vicious cycle that may accelerate motor neuron degeneration in ALS. Microbiota-targeted therapeutic strategies, such as the use of probiotics, postbiotic supplementation (e.g., SCFAs), and fecal microbiota transplantation (FMT), represent promising approaches to restore intestinal homeostasis, reduce neuroinflammation, and potentially slow disease progression.</p>
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16 pages, 3653 KiB  
Article
Two-Dimensional Physical Simulation of the Seepage Law of Microbial Flooding
by Yongheng Zhao, Jianlong Xiu, Lixin Huang, Lina Yi and Yuandong Ma
Energies 2025, 18(5), 1246; https://doi.org/10.3390/en18051246 - 4 Mar 2025
Viewed by 184
Abstract
The study of seepage laws during microbial enhanced oil recovery helps to elucidate the mechanisms behind microbial flooding, and the use of large-scale physical simulation experimental devices can more objectively and accurately investigate the seepage laws of microbes in porous media, and evaluate [...] Read more.
The study of seepage laws during microbial enhanced oil recovery helps to elucidate the mechanisms behind microbial flooding, and the use of large-scale physical simulation experimental devices can more objectively and accurately investigate the seepage laws of microbes in porous media, and evaluate the oil displacement efficiency of microbial systems. In this study, physical simulation experiments of microbial flooding were conducted via a slab outcrop core, and the biochemical parameters such as the concentration of Bacillus subtilis, nutrient concentration, surface tension, and displacement pressure data were tracked and evaluated. The analysis revealed that the characteristics of the pressure field change during microbial flooding and elucidates the migration rules of microbes and nutrients, as well as the change rule of surface tension. The results show that after the microbial system is injected, cells and nutrients are preferentially distributed near the injection well and along the main flow paths, with the bacterial adsorption and retention capacity being greater than those of the nutrient agents. Owing to the action of microorganisms and their metabolites, the overall pressure within the model increased, From the injection well to the production well, the pressure in the model decreases stepwise, and the high-pressure gradient zone is mainly concentrated near the injection well. The fermentation mixture of Bacillus subtilis increased the injection pressure by 0.73 MPa, reduced the surface tension by up to 49.8%, and increased the oil recovery rate by 6.5%. Full article
(This article belongs to the Special Issue Petroleum and Natural Gas Engineering)
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<p>Large-scale physical simulation experimental equipment for MEOR.</p>
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<p>(<b>a</b>) Distribution of the injection and production well network, pressure measurement points, and sampling points; (<b>b</b>) Encapsulated core.</p>
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<p>Flow chart of the large-scale physical simulation experiment for MEOR.</p>
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<p>Distribution and variation in the pressure field during the displacement process.</p>
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<p>Distribution and variation in the microbial field during the displacement process.</p>
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<p>Distribution and variation in nutrients during the displacement process.</p>
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<p>Distribution and variation in surface tension during the displacement process.</p>
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<p>Curves of the oil recovery rate, water cut, and pressure change during the displacement process.</p>
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20 pages, 2561 KiB  
Article
Exploration of Predicted Nitrogen-Containing Natural Products from Northern Canadian Bark Beetle-Associated Bacteria
by Nirasha Atapattu, Nicolas Justus, Hariniha Selvarajan, Mitzchilouise Baylosis, Marc Schieven and Kalindi D. Morgan
Nitrogen 2025, 6(1), 13; https://doi.org/10.3390/nitrogen6010013 - 3 Mar 2025
Viewed by 207
Abstract
Bark beetle-associated bacteria from the sub-boreal and boreal forests of northern Canada represent a largely unexplored source of bioactive natural products. This study aims to investigate the chemical potential of bacteria isolated from Dendroctonus ponderosae, Dendroctonus rufipennis, Dendroctonus pseudotsugae, and [...] Read more.
Bark beetle-associated bacteria from the sub-boreal and boreal forests of northern Canada represent a largely unexplored source of bioactive natural products. This study aims to investigate the chemical potential of bacteria isolated from Dendroctonus ponderosae, Dendroctonus rufipennis, Dendroctonus pseudotsugae, and Ips perturbatus by focusing on nitrogen-containing secondary metabolites. Genomic analyses of the bacterial isolates identified diverse biosynthetic gene clusters (BGCs), including nonribosomal peptides (NRPs), NRPS-PKS hybrids, and ribosomally synthesized and post-translationally modified peptides (RiPPs), many of which exhibit low sequence homology, suggesting potential for novel bioactive compounds. Nitrogen-15 NMR spectroscopy was employed to detect nitrogen-containing functional groups in crude extracts, revealing distinct signals for amides, amines, and nitrogen heterocycles. The combination of BGC predictions and NMR data highlighted the genetic and chemical diversity of these bacteria and underscored the potential for discovering novel nitrogen-rich metabolites. These findings provide a foundation for further exploration of bioactive natural products with pharmaceutical and agrochemical applications and potential to contribute to the understanding of the chemical ecology of bark beetle–microbe interactions in northern ecosystems. Full article
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<p>(<b>a</b>) Terpene BGCs from the sequenced genomes grouped into families utilizing BiG-SCAPE and Cytoscape. (<b>b</b>) RiPP BGCs from the sequenced genomes grouped into families.</p>
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<p>Here, we see gene clusters across all nine NBB isolates containing NRPS modules or free domains with low sequence homology to characterized gene clusters. Gene cluster alignment and identity percentage was completed using clinker. Red-coloured genes indicate NRPS, all other colours randomly assigned by clinker to indicate closely related genes [<a href="#B44-nitrogen-06-00013" class="html-bibr">44</a>].</p>
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<p>Clinker analysis between five BGCs from across the nine isolates and characterized gene clusters in the frontalamide family. Red-coloured genes indicate NRPS, all other colours randomly assigned by clinker to indicate closely related genes. Gene cluster alignment and identity percentage was completed using clinker [<a href="#B44-nitrogen-06-00013" class="html-bibr">44</a>].</p>
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<p>Three BGCs across Streptomyces isolates were identified as bearing sequence homology to the nonribosomal peptide natural products family skyllamycins. (<b>a</b>) The gene cluster alignment between the BGCs from NBB-associated strains and the skyllamycin BGC was completed using clinker. Red-coloured genes indicate NRPS, all other colours randomly assigned by clinker to indicate closely related genes [<a href="#B44-nitrogen-06-00013" class="html-bibr">44</a>]. (<b>b</b>) Module and domain predictions of all BGCs retrieved from antiSMASH and MIBiG.</p>
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18 pages, 4084 KiB  
Article
Investigating the Potential of Native Soil Bacteria for Diesel Biodegradation
by Mihaela Marilena Stancu
Microorganisms 2025, 13(3), 564; https://doi.org/10.3390/microorganisms13030564 - 2 Mar 2025
Viewed by 270
Abstract
In countries with a long petroleum extraction and processing history, such as Romania, extensive soil areas are often polluted with petroleum and its derivatives, posing significant environmental and human health risks. This study explores the diesel biodegradation potential of two native bacterial consortia [...] Read more.
In countries with a long petroleum extraction and processing history, such as Romania, extensive soil areas are often polluted with petroleum and its derivatives, posing significant environmental and human health risks. This study explores the diesel biodegradation potential of two native bacterial consortia isolated from hydrocarbon-polluted soils, focusing on their phenotypic and molecular characteristics, growth kinetics, alkane hydroxylase activity, hydrolase production, and biosurfactant synthesis capabilities. The bacterial consortia, CoP1 and CoP2, were successfully obtained using the standard successive enrichment culture method from two soil samples collected from a region affected by petroleum pollution. The CoP1 and CoP2 consortia demonstrated efficient diesel-degrading capabilities, achieving 50.81−84.32% degradation when cultured in a minimal medium containing 1–10% (v/v) diesel as the sole carbon and energy source. This biodegradation potential was corroborated by their significant alkane hydroxylase activity and the detection of multiple catabolic genes in their genomes. The CoP1 consortium contains at least four catabolic genes (alkB, alkM, todM, ndoM) as well as rhamnosyltransferase 1 genes (rhlAB), while the CoP2 consortium contains only two catabolic genes (ndoM, C23DO). The RND transporter gene (HAE1) was present in both consortia. Secondary metabolites, such as glycolipid-type biosurfactants, as well as extracellular hydrolases (protease, amylase, cellulase, and lipase), were produced by both consortia. The CoP1 and CoP2 consortia demonstrate exceptional efficiency in diesel degradation and biosurfactant production, making them well suited for the bioremediation of soils contaminated with petroleum and its derivatives. Full article
(This article belongs to the Section Microbial Biotechnology)
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<p>Isolation of native bacterial consortia and their quantification. Soil samples P1 and P2 used to initiate enrichment cultures in MSM-petroleum 5%; bacteria quantification in enrichment cultures by most probable number (MPN) method (microplate before and after TTC addition) and plate count agar (PCA) method; isolated bacterial consortia (BCo) CoP1 and CoP2; Petri plates observed under visible and UV light.</p>
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<p>Biodegradation of diesel by native bacterial consortia. CoP1 cultured in MSM-diesel 1% (<span class="html-italic">1</span>), 5% (<span class="html-italic">2</span>), 10% (<span class="html-italic">3</span>); CoP2 cultured in MSM-diesel 1% (<span class="html-italic">4</span>), 5% (<span class="html-italic">5</span>), 10% (<span class="html-italic">6</span>); control (C, uninoculated medium). Cell viability (CV); pyocyanin (PcP) and pyoverdine (PvP) production; Rh6G accumulation; lactose fermentation (LF); Petri plates observed under visible and UV light.</p>
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<p>Enzymatic and protein profile of native bacterial consortia. CoP1 cultured in MSM-diesel 1% (<span class="html-italic">1</span>), 5% (<span class="html-italic">2</span>), 10% (<span class="html-italic">3</span>); CoP2 cultured in MSM-diesel 1% (<span class="html-italic">4</span>), 5% (<span class="html-italic">5</span>), 10% (<span class="html-italic">6</span>). Protease (gelatin hydrolysis, GH); lipase (lipid hydrolysis, LH); amylase (starch hydrolysis, SH); cellulase (cellulose hydrolysis, CH); Petri plates observed under visible and UV light. SDS-PAGE of total-cell protein (TP), broad-range protein molecular weight marker, Promega (M); densitometry plots (DP) for the SDS-PAGE gel.</p>
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<p>Biosurfactant production by native bacterial consortia. CoP1 cultured in MSM-diesel 1% (<span class="html-italic">1</span>), 5% (<span class="html-italic">2</span>), 10% (<span class="html-italic">3</span>); CoP2 cultured in MSM-diesel 1% (<span class="html-italic">4</span>), 5% (<span class="html-italic">5</span>), 10% (<span class="html-italic">6</span>). Diesel overlay (DoO), <span class="html-italic">n</span>-hexadecane overlay (HdO) and <span class="html-italic">n</span>-heptane overlay (HpO); CTAB agar; Petri plates observed under visible and UV light. HPTLC analysis of biosurfactants (Bs), showing the retardation factor (<span class="html-italic">R</span><sub>f</sub>) of chromatographic peaks (arrows) and the sugar standard L-rhamnose (S); TLC plates observed under UV (left, middle) and visible (right) light.</p>
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<p>Biodegradation of diesel by native bacterial consortia. CoP1 cultured in MSM-diesel 1% (<span class="html-italic">1</span>), 5% (<span class="html-italic">2</span>), 10% (<span class="html-italic">3</span>); CoP2 cultured in MSM-diesel 1% (<span class="html-italic">4</span>), 5% (<span class="html-italic">5</span>), 10% (<span class="html-italic">6</span>); control (C, uninoculated medium). HPTLC analysis of residual diesel (RD), showing the retardation factor (<span class="html-italic">R</span><sub>f</sub>) of chromatographic peaks (arrows); TLC plates observed under UV (left, middle) and visible (right) light.</p>
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20 pages, 4072 KiB  
Article
Green Synthesis and Characterization of Silver Nanoparticles from Tinospora cordifolia Leaf Extract: Evaluation of Their Antioxidant, Anti-Inflammatory, Antibacterial, and Antibiofilm Efficacies
by Vijaya Durga V. V. Lekkala, Arun Vasista Muktinutalapati, Veeranjaneya Reddy Lebaka, Dakshayani Lomada, Mallikarjuna Korivi, Wei Li and Madhava C. Reddy
Nanomaterials 2025, 15(5), 381; https://doi.org/10.3390/nano15050381 - 1 Mar 2025
Viewed by 1558
Abstract
The use of metal nanoparticles is gaining popularity owing to their low cost and high efficacy. We focused on green synthesis of silver nanoparticles (AgNPs) using Tinospora cordifolia (Tc) leaf extracts. The structural characteristics of Tc nanoparticles (TcAgNPs) were determined using several advanced [...] Read more.
The use of metal nanoparticles is gaining popularity owing to their low cost and high efficacy. We focused on green synthesis of silver nanoparticles (AgNPs) using Tinospora cordifolia (Tc) leaf extracts. The structural characteristics of Tc nanoparticles (TcAgNPs) were determined using several advanced techniques. Pharmacological activities, including antioxidant, anti-inflammatory, and antibacterial properties, were evaluated through in vitro studies. In the results, the change in sample color from yellow to brown after adding silver nitrate revealed the synthesis of TcAgNPs, and the UV–visible spectrum confirmed their formation. X-ray diffraction studies showed the presence of reducing agents and the crystalline nature of the nanoparticles. Fourier-transform infrared spectra revealed the existence of essential secondary metabolites, which act as reducing/capping agents and stabilize the nanoparticles. The size of the TcAgNPs was small (range 36–168 nm) based on the measurement method. Their negative zeta potential (−32.3 mV) ensured their stability in water suspensions. The TcAgNPs were predominantly spherical, as evidenced from scanning electron microscopy and transmission electron microscopy. Atomic absorption spectroscopy data further revealed the conversion of silver nitrate into silver nanoparticles, and thermogravimetric analysis data showed their thermal stability. The TcAgNPs showed significant DPPH/ABTS radical scavenging ability in a concentration-dependent manner (25–100 µg/mL). Membrane lysis assays showed an effective anti-inflammatory activity of the TcAgNPs. Furthermore, the TcAgNPs showed potent antibacterial effects against multidrug-resistant bacteria (Pseudomonas aeruginosa, Klebsiella pneumonia, Escherichia coli, and Staphylococcus aureus). The TcAgNPs treatment also exhibited antibiofilm activity against bacterial strains, in a concentration-dependent manner. Our findings demonstrate the structural characteristics of green-synthesized TcAgNPs using advanced techniques. TcAgNPs can be developed as potential antioxidant, anti-inflammatory, and antibacterial drugs. Full article
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<p>Pictorial illustration of green-synthesis of AgNPs from <span class="html-italic">Tinospora cordifolia</span> leaf extracts.</p>
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<p>UV-visible spectroscopy of green-synthesized TcAgNPs at different time intervals represented as (<b>a</b>–<b>e</b>), at 2, 4, 6, 12, and 24 h respectively. (<b>e</b>) included with leaf extracts.</p>
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<p>Structural characteristics of TcAgNPs using (<b>a</b>) XRD, (<b>b</b>) FTIR, (<b>c</b>) DLS (hydrodynamic size), and (<b>d</b>) zeta potential analyses.</p>
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<p>Structural characteristics of TcAgNPs: (<b>a</b>) scanning electron microscope image: spherical structures of TcAgNPs are labeled with a red outline; (<b>b</b>) energy dispersive X-ray spectrum showing the presence of silver; (<b>c</b>) elemental composition of TcAgNPs from EDX analysis.</p>
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<p>TEM images and particle size distribution histogram of TcAgNPs at different magnifications.</p>
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<p>TGA thermograph of <span class="html-italic">T. cordifolia</span> leaf extracts and TcAgNPs.</p>
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<p>Silver concentration (ppm) in green-synthesized silver nanoparticles.</p>
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<p>Antioxidant properties of different concentrations of TcAgNPs: (<b>a</b>) DPPH radical scavenging activity and (<b>b</b>) ABTS radical scavenging activity. (<b>c</b>) Anti-inflammatory activity of different concentrations of TcAgNPs. Values expressed as mean ± standard deviation.</p>
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<p>Zones of inhibition with different concentrations of TcAgNPs against multidrug-resistant (MDR) bacteria: (<b>a</b>) <span class="html-italic">Pseudomonas aeruginosa</span>, (<b>b</b>) <span class="html-italic">Klebsiella pneumonia</span>, (<b>c</b>) <span class="html-italic">Escherichia coli</span>, and (<b>d</b>) <span class="html-italic">Staphylococcus aureus</span>. (<b>e</b>) Graphical presentation of zones of inhibition of TcAgNPs against MDR bacteria. Values are expressed as the mean ± standard deviation.</p>
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<p>Antibiofilm activity with different concentrations of TcAgNPs: (<b>a</b>) MIC and MBC efficacy of TcAgNPs. (<b>b</b>) Percentage inhibition ability of TcAgNPs and antibiotic against biofilm formation of <span class="html-italic">P. aeruginosa</span>, <span class="html-italic">K. pneumonia</span>, <span class="html-italic">E. coli</span>, and <span class="html-italic">S. aureus.</span> Values are expressed as the mean ± standard deviation.</p>
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16 pages, 1660 KiB  
Article
Low Plasma Choline, High Trimethylamine Oxide, and Altered Phosphatidylcholine Subspecies Are Prevalent in Cystic Fibrosis Patients with Pancreatic Insufficiency
by Wolfgang Bernhard, Anna Shunova, Julia Boriga, Ute Graepler-Mainka and Johannes Hilberath
Nutrients 2025, 17(5), 868; https://doi.org/10.3390/nu17050868 - 28 Feb 2025
Viewed by 158
Abstract
Background: Exocrine pancreatic insufficiency in cystic fibrosis (CF) increases fecal choline losses, but the postnatal course of plasma choline and its metabolites in these patients is unknown. While choline homeostasis is crucial for cellular, bile, and lipoprotein metabolism, via phosphatidylcholine (PC) and via [...] Read more.
Background: Exocrine pancreatic insufficiency in cystic fibrosis (CF) increases fecal choline losses, but the postnatal course of plasma choline and its metabolites in these patients is unknown. While choline homeostasis is crucial for cellular, bile, and lipoprotein metabolism, via phosphatidylcholine (PC) and via betaine as a methyl donor, choline deficiency is associated with impaired lung and liver function, including hepatic steatosis. Objective: The goal of our study was to evaluate the plasma levels of choline, betaine, trimethylamine oxide (TMAO), PC, and PC subclasses in CF patients from infancy to adulthood and compare those with exocrine pancreatic insufficiency (EPI) to those with pancreatic sufficiency (EPS). Methods: Retrospective analysis of target parameters in plasma samples (July 2015–November 2023) of CF patients (0.64–24.6 years) with tandem mass spectrometry. Results: A total of 477 samples from 162 CF patients were analyzed. In CF patients with EPI (N = 148), plasma choline and betaine concentrations were lower and decreased with age compared to EPS patients showing normal values. TMAO concentrations, indicating intestinal choline degradation by bacterial colonization, were frequently elevated in EPI from infancy onwards, and inversely related to plasma choline and betaine levels. PC-containing linoleic acid levels were lower in EPI, but arachidonic and docosahexaenoic acid content was similar in both patient groups. Conclusion: CF patients with EPI are at risk of choline and betaine deficiency compared to exocrine pancreas-sufficient CF patients. Elevated TMAO concentrations in EPI patients indicate increased bacterial colonization leading to choline degradation before absorption. These findings indicate that laboratory testing of choline, betaine, and TMAO as well as clinical trials on choline supplementation are warranted in CF patients. Full article
(This article belongs to the Section Clinical Nutrition)
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<p>Flow chart of sample and patient inclusion.</p>
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<p>Plasma concentrations of choline and its metabolites in CF patients with (EPI) and without (EPS) exocrine pancreatic insufficiency. Choline (<b>A</b>), betaine (<b>B</b>), and trimethylamine oxide (TMAO) (<b>C</b>) are shown in relation to age. (<b>D</b>) shows the direct correlation between choline and betaine as well as the inverse relation between choline and TMAO in plasma. Data are median values of individual CF patients with (N = 148) and without (N = 14) exocrine pancreas insufficiency. Abbreviations: ρ, Spearman’s correlation coefficient; <span class="html-italic">p</span>, significance level.</p>
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<p>Plasma phosphatidylcholine (PC) of CF patients with (EPI) and without (EPS) exocrine pancreatic insufficiency. (<b>A</b>) shows concentrations of total PC, whereas (<b>B</b>–<b>D</b>) show its fractions of PC containing 2 saturated (Disat.-PC), an oleic acid (OA-PC) or a linoleic acid (LA-PC) residue. Data are median values of individual CF patients with (N = 148) and without (N = 14) exocrine pancreas insufficiency. Abbreviations: ρ, Spearman correlation coefficient; <span class="html-italic">p</span>, significance level.</p>
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<p>Fractions of total plasma phosphatidylcholine (PC) containing an arachidonic (ARA-PC) (<b>A</b>), eicosapentaenoic acid (EPA-PC) (<b>B</b>) or docosahexaenoic (DHA-PC) (<b>C</b>) acid residue. (<b>D</b>) shows the ratio of <span class="html-italic">n</span>-6 LC-PUFA vs. <span class="html-italic">n</span>-3 LC-PUFA-PC (ARA-PCvs.EPA-PC + DHA-PC). Data are median values of individual CF patients with (N = 148) and without (N = 14) exocrine pancreas insufficiency. Abbreviations: ρ—Spearman correlation coefficient; <span class="html-italic">p</span>—significance level.</p>
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22 pages, 5786 KiB  
Article
Microbiome and Metabolome Illustrate the Correlations Between Endophytes and Flavor Metabolites in Passiflora ligularis Fruit Juice
by Meijun Qi, Xuedong Shi, Wenlong Huang, Qilong Wei, Zhenwei Zhang, Rongqi Zhang, Shilang Dong, Sumera Anwar, Hafiz Faiq Bakhat, Butian Wang and Yu Ge
Int. J. Mol. Sci. 2025, 26(5), 2151; https://doi.org/10.3390/ijms26052151 - 27 Feb 2025
Viewed by 181
Abstract
This study investigates the interplay between volatile and non-volatile flavor metabolites and endophytic microbial communities during three developmental stages of Passiflora ligularis fruit juice. Using bioinformatics and metabolomics, we characterize microbial diversity and metabolic variations to understand flavor development. A total of 1490 [...] Read more.
This study investigates the interplay between volatile and non-volatile flavor metabolites and endophytic microbial communities during three developmental stages of Passiflora ligularis fruit juice. Using bioinformatics and metabolomics, we characterize microbial diversity and metabolic variations to understand flavor development. A total of 1490 bacterial and 1158 fungal operational taxonomic units (OTUs) were identified. Young fruits had higher microbial diversity, dominated by Proteobacteria and Firmicutes (bacteria) and Ascomycota and Basidiomycota (fungi). As the fruit matured, Proteobacteria increased while Firmicutes decreased, indicating that microbial succession is tied to development. Metabolomic profiling identified 87 volatile and 1002 non-volatile metabolites, with distinct chemical classes varying across stages. Saturated hydrocarbons and fatty alcohols were the main volatile metabolites, while organic acids and lipids among non-volatile metabolites showed stage-dependent changes, influencing flavor complexity. Correlation analysis showed microbial-flavor interactions: Proteobacteria negatively correlated with metabolites, while Firmicutes positively correlated with metabolites. Ascomycota positively correlated with volatile metabolites, whereas Basidiomycota showed an inverse relationship, highlighting their differential contributions to flavor biosynthesis. This study enhances understanding of microbial and metabolic factors shaping P. ligularis fruit flavor, highlighting the importance of microbial influence on fruit metabolomics. The findings suggest the potential for microbiome engineering to improve flavor quality, aiding postharvest management and industrial processing in the food and beverage industry. Full article
(This article belongs to the Special Issue The Molecular Research of Plant and Microbial Communities)
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<p>Venn diagrams illustrating the distribution of (<b>A</b>) bacterial and (<b>B</b>) fungal OTUs in the fruit juices of three developmental stages of <span class="html-italic">P. ligularis</span>. S1, S2, and S3 represent the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting), respectively. The number within each region denotes the OUT counts, while the circle’s areas are not drawn to scale.</p>
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<p>Relative abundance of bacterial communities in the fruit juices of three developmental stages of <span class="html-italic">P. ligularis</span>. (<b>A</b>) Stacked bar chart displaying bacterial phyla across S1, S2, and S3. (<b>B</b>) Stacked bar chart showing bacterial genus-level composition across S1, S2, and S3. The analysis was based on 16S rRNA sequencing. S1, S2, and S3 represent the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting), respectively.</p>
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<p>Relative abundance of fungal communities in the fruit juices of three developmental stages of <span class="html-italic">P. ligularis</span>. (<b>A</b>) Stacked bar chart displaying fungal phyla across S1, S2, and S3. (<b>B</b>) Stacked bar chart showing fungal genus-level composition. The analysis was based on ITS sequencing. S1, S2, and S3 represent the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting), respectively.</p>
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<p>Alpha diversity indices, (<b>a</b>) Shannon, (<b>b</b>) Simpson, and (<b>c</b>) Chao1 of the endophytic bacterial and fungal communities in the developing <span class="html-italic">P. ligularis</span> fruit juice. S1, S2, and S3 represent the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting) of <span class="html-italic">P. ligularis</span> fruit, respectively. Different letters in columns indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3).</p>
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<p>Principal coordinate analysis (PCoA) of the endophytic bacterial and fungal communities in the fruit juices of three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) PCoA of the endophytic bacterial community. (<b>B</b>) PCoA of the endophytic fungal community. S1, S2, and S3 represent the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting) of <span class="html-italic">P. ligularis</span> fruit, respectively.</p>
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<p>Volcano plots illustrate differentially accumulated volatile flavor metabolites across three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) S1 vs. S2, (<b>B</b>) S1 vs. S3, and (<b>C</b>) S2 vs. S3. S1, S2, and S3 correspond to the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting), respectively.</p>
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<p>Volcano plots illustrate differentially accumulated non-volatile flavor metabolites across three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) S1 vs. S2, (<b>B</b>) S1 vs. S3, and (<b>C</b>) S2 vs. S3. S1, S2, and S3 correspond to the young fruit stage (0–45 days after fruit setting), coloration stage (46–59 days after fruit setting), and maturity stage (60–79 days after fruit setting), respectively.</p>
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<p>KEGG pathway enrichment analysis of differentially accumulated non-volatile metabolites in <span class="html-italic">Passiflora ligularis</span> fruit across three developmental stages. The y-axis represents the top 20 significantly enriched KEGG pathways, while the x-axis indicates the RichFactor (the ratio of the number of differentially accumulated metabolites annotated in a pathway to the total number of metabolites in that pathway). The size of the dots represents the number of metabolites mapped to each pathway, while the color represents the <span class="html-italic">q</span>-value (adjusted <span class="html-italic">p</span>-value), with red indicating more significant enrichment.</p>
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<p>Correlations between 28 differentially abundant volatile flavor metabolites and the dominant endophytic microbiota with a relative abundance of ≥10% across three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) Correlations with two dominant endophytic bacterial phyla. (<b>B</b>) Correlations with two dominant endophytic fungal phyla. * and ** indicate significant differences at <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>Correlations between 28 differentially abundant volatile flavor metabolites and the endophytic microbiota in the fruit juices of three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) Correlations with three dominant bacterial genera. (<b>B</b>) Correlations with three dominant fungal genera. *, **, and *** indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Correlations between 40 differentially abundant non-volatile flavor metabolites and the dominant endophytic microbiota (with a mean relative abundance of ≥10%) across three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) Correlations with the two dominant endophytic bacterial phyla. (<b>B</b>) Correlations with the two dominant endophytic fungal phyla. *, **, and *** indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Correlations between 40 differentially abundant non-volatile flavor metabolites and the dominant endophytic microbiota (with a mean relative abundance of ≥10%) across three developmental stages of <span class="html-italic">P. ligularis</span> fruit. (<b>A</b>) Correlations with the three dominant bacterial genera. (<b>B</b>) Correlations with the three dominant fungal genera. *, **, and *** indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Three stages of development of <span class="html-italic">P. ligularis</span> fruit. (<b>S1</b>) Young fruit stage (45 days after fruit setting), (<b>S2</b>) coloration stage (60 days after fruit setting), (<b>S3</b>) maturity stage (80 days after fruit setting).</p>
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10 pages, 837 KiB  
Review
Microbiome-Based Therapeutics for Salt-Sensitive Hypertension: A Scoping Review
by Abdulwhab Shremo Msdi, Anahita Haghparast, Kevin W. Garey and Elisabeth M. Wang
Nutrients 2025, 17(5), 825; https://doi.org/10.3390/nu17050825 - 27 Feb 2025
Viewed by 255
Abstract
The purpose of this scoping review was to provide a comprehensive understanding of the current knowledge concerning the gut microbiome and SCFAs as emerging treatments for salt-sensitive hypertension. Relevant animal and human studies were identified via PubMed through August 2024. Twenty-four human ( [...] Read more.
The purpose of this scoping review was to provide a comprehensive understanding of the current knowledge concerning the gut microbiome and SCFAs as emerging treatments for salt-sensitive hypertension. Relevant animal and human studies were identified via PubMed through August 2024. Twenty-four human (n = 9) and animal (n = 15) trials were included. Most human studies were observational (n = 6), aiming to compare gut microbiota differences between hypertensive and normotensive individuals. Three human studies evaluated microbiome-based interventions either via a sodium-restricted diet (n = 2) or prebiotic supplementation (n = 1). Fifteen animal trials involving either mice or rats were identified, all of which were interventional. These included dietary changes (n = 9), probiotic treatments (n = 1), postbiotic primarily bacterial metabolites (n = 4), and live biotherapeutic products (n = 4). All interventions were effective in decreasing blood pressure. Microbiome-based therapies as biologic modifiers for salt-sensitive hypertension are emerging. Substantial knowledge gaps remain, warranting further research to fully explore this promising therapeutic avenue. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>Study selection flow diagram. * Review studies’ bibliographies were screened for relevant primary studies.</p>
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<p>Illustration of the conversion of indigestible fiber into short-chain fatty acids and subsequent systemic impact via G-protein-coupled receptors. Straight arrow (Blue) indicates direct activation. Dashed arrow (Brown) indicates negative feedback loop.</p>
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31 pages, 5088 KiB  
Review
Advances in Wearable Biosensors for Wound Healing and Infection Monitoring
by Dang-Khoa Vo and Kieu The Loan Trinh
Biosensors 2025, 15(3), 139; https://doi.org/10.3390/bios15030139 - 23 Feb 2025
Viewed by 674
Abstract
Wound healing is a complicated biological process that is important for restoring tissue integrity and function after injury. Infection, usually due to bacterial colonization, significantly complicates this process by hindering the course of healing and enhancing the chances of systemic complications. Recent advances [...] Read more.
Wound healing is a complicated biological process that is important for restoring tissue integrity and function after injury. Infection, usually due to bacterial colonization, significantly complicates this process by hindering the course of healing and enhancing the chances of systemic complications. Recent advances in wearable biosensors have transformed wound care by making real-time monitoring of biomarkers such as pH, temperature, moisture, and infection-related metabolites like trimethylamine and uric acid. This review focuses on recent advances in biosensor technologies designed for wound management. Novel sensor architectures, such as flexible and stretchable electronics, colorimetric patches, and electrochemical platforms, enable the non-invasive detection of changes associated with wounds with high specificity and sensitivity. These are increasingly combined with AI and analytics based on smartphones that can enable timely and personalized interventions. Examples are the PETAL patch sensor that applies multiple sensing mechanisms for wide-ranging views on wound status and closed-loop systems that connect biosensors to therapeutic devices to automate infection control. Additionally, self-powered biosensors that tap into body heat or energy from the biofluids themselves avoid any external batteries and are thus more effective in field use or with limited resources. Internet of Things connectivity allows further support for remote sharing and monitoring of data, thus supporting telemedicine applications. Although wearable biosensors have developed relatively rapidly and their prospects continue to expand, regular clinical application is stalled by significant challenges such as regulatory, cost, patient compliance, and technical problems related to sensor accuracy, biofouling, and power, among others, that need to be addressed by innovative solutions. The goal of this review is to synthesize current trends, challenges, and future directions in wound healing and infection monitoring, with emphasis on the potential for wearable biosensors to improve patient outcomes and reduce healthcare burdens. These innovations are leading the way toward next-generation wound care by bridging advanced materials science, biotechnology, and digital health. Full article
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<p>Stages of wound healing. Created with mindthegraph.com.</p>
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<p>Regulators of wound healing and fibrosis. The timing, overlap, and intensity of activation in each phase of wound healing are governed by several molecular, biological, and mechanical variables. The image illustrates how each of these elements influences wound healing. Blue indicates activation, while pink denotes attenuation of fibrosis. Copyright MDPI (2020) [<a href="#B29-biosensors-15-00139" class="html-bibr">29</a>].</p>
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<p>Schematic of the PETAL sensor, a wound healing monitoring sensor working without a battery. (<b>A</b>) A graphical abstract of the sensor adhered onto a burn wound for colorimetric analysis of wound healing progress. (<b>B</b>) The real sensor patch and the multiplexed sensing targets/principles. (<b>C</b>) The shape and dimension of the sensor patch compared to a 50 cent Singapore coin. (<b>D</b>) Schematic of neural network-based machine learning algorithm used for wound classification. Copyright AAAS (2023) [<a href="#B50-biosensors-15-00139" class="html-bibr">50</a>].</p>
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<p>Fabrication and application of flexible carbon ultramicroelectrode arrays (CUAs) in electrochemical detection of multianalyte biomarkers for wound monitoring. Detection of pyocyanin (PYO), uric acid, and nitric oxide (NO•) as biomarkers in simulated wound media. Monitoring pathogen–host interactions and the effects of silver ions (Ag<sup>+</sup>) on PYO secretion by Pseudomonas aeruginosa. Quantification of cellular NO• from immune cells in the wound matrix using flexible CUAs. Copyright ACS Publications (2020) [<a href="#B165-biosensors-15-00139" class="html-bibr">165</a>].</p>
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<p>Bacteria-activated dual pH- and temperature-responsive hydrogel for infection control and wound healing. (<b>a</b>) Schematic of hydrogel cross-linked with <span class="html-italic">N</span>-isopropylacrylamide and acrylic acid, loaded with ultrasmall silver nanoparticles (AgNPs). (<b>b</b>) pH- and temperature-triggered Ag<sup>+</sup> ion release, with restricted release at acidic pH (&lt;5.5) and &gt;90% release at alkaline pH (&gt;7.4). (<b>c</b>) In vivo studies demonstrating clearance of Staphylococcus aureus infection and significantly accelerated wound healing. Copyright ACS Publications (2022) [<a href="#B171-biosensors-15-00139" class="html-bibr">171</a>].</p>
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<p>A bioresorbable pH sensor for wireless monitoring of pH. (<b>A</b>) Illustration for medical application of the proposed sensor in locally monitoring gastric leakage after LSG surgery. (<b>B</b>) Structure of the pH sensor and its compositions. (<b>C</b>) Experimental and simulation results of pH-triggered physical expansion of the sensor after 2 h of immersion in solutions of varying pH. Copyright AAAS (2024) [<a href="#B204-biosensors-15-00139" class="html-bibr">204</a>].</p>
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22 pages, 4818 KiB  
Article
Integrated Transcriptomic and Metabolomic Analyses Reveal the Importance of the Terpenoid, Fatty Acid, and Flavonoid Pathways in Rice Cell Death and Defense
by Pengfei Bai, Yanfang Liu, Laisa Gomes-Dias, Rachel Combs-Giroir, Shaoxing Dai, Naeyeoung Choi, Yun Lin, Matthew Bernier, Emmanuel Hatzakis, Guo-Liang Wang and Joshua J. Blakeslee
Plants 2025, 14(5), 665; https://doi.org/10.3390/plants14050665 - 21 Feb 2025
Viewed by 265
Abstract
Lesion mimic mutants provide unique tools to investigate plant–pathogen interactions, often exhibiting hypersensitive responses in the absence of biotic or abiotic stresses. The overexpression of the S-domain receptor-like kinase gene, SPL11 cell-death suppressor 2 (SDS2), in rice leads [...] Read more.
Lesion mimic mutants provide unique tools to investigate plant–pathogen interactions, often exhibiting hypersensitive responses in the absence of biotic or abiotic stresses. The overexpression of the S-domain receptor-like kinase gene, SPL11 cell-death suppressor 2 (SDS2), in rice leads to constitutive programmed cell death and enhanced resistance to fungal and bacterial pathogens. However, the mechanisms underlying this broad-spectrum resistance remain unclear. This study integrates transcriptomic and metabolomic analyses of the SDS2-ACT mutant to uncover gene expression and metabolic shifts associated with disease resistance. To identify SDS2-specific physiological changes related to pathogen resistance, leaf tissues from the SDS2-ACT mutant and the Kitkaake WT line were subjected to both transcriptomic and non-targeted metabolic profiling. Transcriptomic analyses identified 1497 differentially expressed genes (DEGs), including up-regulated genes involved in terpenoid and flavonoid biosynthesis, phytohormone signaling, and defense-related pathways (including pathogenesis-related [PR] genes). Metabolomic profiling revealed significant alterations in the accumulation of several compound classes, including putative: terpenoids, phenylpropanoids, phytohormones, fatty acids, and sugars. These changes are likely correlated with the observed cell death and resistance phenotypes in the SDS2-ACT mutant. This study provides an overall landscape of the transcriptomic and metabolomic alterations in a lesion mimic mutant, identifying candidate defense-related genes and metabolites for functional analysis in rice. Full article
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<p>Transcriptomic profiling of rice mutant SDS2-ACT with constitutively activated immunity and cell death. (<b>A</b>) Volcano plot of all differentially expressed genes (DEGs) in SDS2-ACT compared to wild-type (WT) rice. The y-axis represents the significance level (-log10(<span class="html-italic">p</span>-value)), and the x-axis shows the log2(fold change). Green dots indicate down-regulated genes, and red dots indicate up-regulated genes with significant fold change &gt;2 and <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>) Bar chart showing the number of DEGs. A total of 378 genes were down-regulated, and 1119 genes were up-regulated in SDS2-ACT compared to WT, indicating a significant shift in gene expression patterns. (<b>C</b>) Heatmap representation of DEGs in SDS2-ACT vs. WT rice. The heatmap is a result of one-dimensional hierarchical clustering of the 378 down-regulated and 1119 up-regulated DEGs. Columns represent three independent biological replicates. (<b>D</b>) Gene Ontology (GO) analysis of the 378 down-regulated and 1119 up-regulated DEGs highlights the enrichment of defense and cell death-related pathways. The dot plot shows the top enriched GO terms in biological processes, with the size of the dots representing fold enrichment and color indicating the false discovery rate (FDR).</p>
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<p>KEGG pathway analysis and gene-concept network visualization (<b>A</b>). (<b>A</b>) KEGG pathway enrichment analysis of differentially expressed genes (DEGs), highlighting potentially enriched metabolic pathways. The bar chart shows the gene ratio for each significantly enriched pathway, including the biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, and glutathione metabolism. The size of the dots represents the gene count, and the color indicates the <span class="html-italic">p</span>-value, with red indicating higher significance. (<b>B</b>) Gene-concept network. The network diagram visualizes the connections between multiple biological categories and the specific DEGs involved in those pathways. Nodes represent KEGG pathways, and edges represent DEGs connected to these pathways. Node size indicates the number of genes involved, and the color of the edges represents the fold change in gene expression.</p>
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<p>Comparative analysis of DEGs from SDS2-ACT vs. WT with DEGs from NPB and NPB-Piz-t rice groups sprayed with <span class="html-italic">Magnaporthe oryzae</span> strain <span class="html-italic">RO1-1</span> (<b>A</b>). (<b>A</b>) The Venn diagram highlights the overlapping and unique DEGs between the SDS2-ACT vs. WT comparison and the compatible/incompatible interaction from NPB and NPB-Piz-t rice infected with <span class="html-italic">M. oryzae</span> strain <span class="html-italic">RO1-1</span>. There are 766 common DEGs identified across these conditions. (<b>B</b>). The 766 common DEGs from the Venn diagram were subjected to GO term enrichment analysis. The analysis identified several significantly enriched defense-related GO terms, emphasizing the role of these DEGs in the plant’s defense mechanisms. The enriched GO terms include processes related to immune response, signal transduction, and cellular defense mechanisms, highlighting their potential involvement in the enhanced defense response in the SDS2-ACT mutant.</p>
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<p>Metabolomic profiling of rice mutant SDS2-ACT compared to WT. (<b>A</b>) Volcano plot representing differential metabolite levels between the SDS2-ACT mutant and WT plants. Metabolites with significant up-regulation are depicted in green, while significantly down-regulated metabolites are shown in red. Black dots indicate metabolites with no significant changes. (<b>B</b>) Principal component analysis (PCA) plot demonstrating the separation of metabolomic profiles between the SDS2-ACT mutants (red) and WT plants (blue). (<b>C</b>) Heatmap illustrating the concentration patterns of differentially abundant metabolites in the SDS2-ACT mutants compared to WT plants. Rows represent individual metabolites, and columns represent biological replicates.</p>
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<p>Combined transcriptomic and metabolomic pathway analysis in SDS2-ACT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of transcriptomic and metabolomic data in SDS2-ACT vs. WT. (<b>A</b>) Pathway analysis showed altered levels of expression of genes involved in diterpene biosynthesis in SDS2-ACT vs. WT. (<b>B</b>) Pathway analysis showed altered expression of genes involved in carotenoid metabolites in SDS2-ACT vs. WT. A, B. Red boxes indicate up-regulated genes, while green boxes indicate down-regulated genes. Circles indicate specific metabolites. Yellow circles indicate metabolites with increased accumulation in SDS2-ACT, as determined by LC-MS analyses; blue circles indicate metabolites with decreased accumulation in SDS2-ACT vs. WT.</p>
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24 pages, 3005 KiB  
Article
A Chalcone Synthase-like Bacterial Protein Catalyzes Heterocyclic C-Ring Cleavage of Naringenin to Alter Bioactivity Against Nuclear Receptors in Colonic Epithelial Cells
by Ebru Ece Gülşan, Farrhin Nowshad, Meredith Davis Leigh, Jimmy Walter Crott, Hyejin Park, Greg Martin, Stephen Safe, Robert S. Chapkin, Arul Jayaraman and Kyongbum Lee
Metabolites 2025, 15(3), 146; https://doi.org/10.3390/metabo15030146 - 21 Feb 2025
Viewed by 239
Abstract
Gut microbial metabolism of dietary flavonoids leads to a diverse array of bioactive products that are closely associated with human health. Combining enzyme promiscuity prediction, metabolomics, and in vitro model systems, we identified a chalcone-synthase-like bacterial polyketide synthase that can initiate the metabolism [...] Read more.
Gut microbial metabolism of dietary flavonoids leads to a diverse array of bioactive products that are closely associated with human health. Combining enzyme promiscuity prediction, metabolomics, and in vitro model systems, we identified a chalcone-synthase-like bacterial polyketide synthase that can initiate the metabolism of naringenin by catalyzing the C-ring cleavage. This was validated using a mutant strain of the model organism Bacillus subtilis (ATCC 23857). Our prediction–validation methodology could be used to systematically characterize the products of gut bacterial flavonoid metabolism and identify the responsible enzymes and species. In vitro experiments with Caco-2 cells revealed that naringenin and its bacterial metabolites differentially engage the aryl hydrocarbon receptor (AhR) and orphan nuclear receptor 4A (NR4A). These results suggest that metabolism by gut bacterial species could directly impact the profile of bioactive flavonoids and influence inflammatory responses in the intestine. These results are significant for understanding gut-microbiota-dependent physiological effects of dietary flavonoids. Full article
(This article belongs to the Section Cell Metabolism)
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<p>Comparison of reaction products predicted by PROXIMAL, Way2Drug, and BioTransformer. Five flavonoids were selected to represent the following subclasses: flavonol (quercetin), flavanone (naringenin), flavone (apigenin and luteolin), and isoflavone (genistein). Coverage was calculated with respect to the total number of distinct reaction products collectively predicted by the three tools. Full (100%) coverage by a tool for a reaction type indicates that the tool predicted all metabolites predicted by the other two tools for the same reaction type.</p>
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<p>Structural similarity and predicted reaction similarity of flavonoids. (<b>A</b>) Multidimensional scaling (MDS) map for 15 flavonoid aglycones, where the compounds’ coordinates were assigned based on a matrix of relative pairwise distances representing structural dissimilarities calculated using the SIMCOMP2 tool. Symbols and colors indicate the compounds’ subclasses. (<b>B</b>) MDS map where the compounds’ coordinates were assigned based on their predicted reaction patterns. (<b>C</b>) Correlation between structural and reaction dissimilarities of flavonoids. Solid and dashed lines show the best fit linear regression model and 95% confidence intervals, respectively.</p>
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<p>Distribution of predicted flavonoid metabolizing enzymes across different bacterial phyla. The color scale corresponds to the number of matching enzymes in the phylum. The number of matches for a phylum was normalized by the number of strains included in the model for the phylum. Each row corresponds to a different combination of phylum and type of reaction.</p>
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<p>Concentrations of 3-(4-hydroxyphenyl) propionic acid (3,4-HPPA) in naringenin-treated bacterial monocultures. (<b>A</b>) <span class="html-italic">F. plautii</span>, (<b>B</b>) <span class="html-italic">E. coli</span>, (<b>C</b>) <span class="html-italic">P. lactis</span>, (<b>D</b>) <span class="html-italic">L. plantarum</span>, (<b>E</b>) wild-type <span class="html-italic">B. subtilis</span>, and (<b>F</b>) mutant <span class="html-italic">B. subtilis</span> lacking chalcone synthase (Δ<span class="html-italic">bcsA</span>). Data shown are means ± SD (N = 3 biological replicates). Asterisks (*) indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) compared to the vehicle control (0 µM naringenin) at the corresponding time point.</p>
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<p>Conversion of naringenin to phloretin and phloretin-(hydroxyphenyl-13C6) to 13C6-3,4-HPPA in fecal culture. (<b>A</b>) Proposed pathway for naringenin metabolism via C-ring cleavage in the fecal culture. The dotted arrow shows direct conversion of naringenin to phloretin via a flavanone-cleaving reduction of the C-ring. (<b>B</b>) Concentration of naringenin in the fecal culture at different times after naringenin supplementation. An asterisk (*) indicates a significant difference compared to the initial timepoint at 12 h. (<span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Phloretin-(hydroxyphenyl-13C6) and (<b>D</b>) 13C6-3,4-HPPA concentrations at different times after 100 µM phloretin-(hydroxyphenyl-13C6) supplementation. Data shown are means ± SD (N = 3 biological replicates). An asterisk (*) indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) compared to the vehicle control (0 µM) at the corresponding time point.</p>
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<p>Induction of AhR-responsive genes by naringenin (Nar) and naringenin chalcone (Chalc). (<b>A</b>) CYP1A1, (<b>B</b>) CYP1B1, and (<b>C</b>) UGT1A1 in Caco-2 cells. (<b>D</b>) Cyp1a1, (<b>E</b>) Cyp1b1, and (<b>F</b>) Ugt1a1 in YAMC cells. (<b>G</b>–<b>I</b>) Induction of CYP1A1, CYP1B1, and UGT1A1 in Caco-2 cells by 3,4-HPPA alone and in combination with 10 nM 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). The cells were treated with varying concentrations of the indicated compounds for 24 h. Gene (mRNA) expression was determined by real-time PCR. The data shown are means ± SD (N = 3 biological replicates). For panels (<b>A</b>–<b>F</b>), asterisks indicate a significant difference (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01) compared to vehicle control (DMSO), whereas hashes (#) indicate a significant difference (#, <span class="html-italic">p</span> &lt; 0.05; ##, <span class="html-italic">p</span> &lt; 0.01) between naringenin and naringenin chalcone at the same concentration. For panels G–I, an asterisk (*) indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) compared to TCDD, which was used as the positive control for AhR-dependent induction of gene expression.</p>
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<p>Loss of naringenin ligand binding and immunomodulatory activity upon metabolism to 3,4-HPPA. Concentration-dependent quenching of tryptophan fluorescence in the ligand-binding domain (LBD) of NR4A1 with (<b>A</b>) naringenin and (<b>B</b>) 3,4-HPPA. (<b>C</b>) IL-8 concentration in conditioned medium following IL-1β stimulation of Caco-2 cells treated with naringenin, 3,4-HPPA, and phloretin. The data shown in panel (C) are means ± SD (N = 3 biological replicates). An asterisk (*) indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) compared to the positive control.</p>
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17 pages, 5923 KiB  
Article
Integrated Microbiome and Metabolomics Analysis Reveals Altered Aggressive Behaviors in Broiler Chickens Showing Different Tonic Immobility
by Jiang Gao, Xiaoxian Cheng, Xuanfu Wu, Cunzhi Zou, Bin He and Wenqiang Ma
Animals 2025, 15(4), 601; https://doi.org/10.3390/ani15040601 - 19 Feb 2025
Viewed by 177
Abstract
Tonic immobility (TI) serves as an indicator of innate stress response recovery in poultry. Broilers with different TI phenotypes exhibit varying levels of aggressive behavior, which can significantly impact their welfare. However, the influences of TI phenotypes on broiler aggression remain largely unexplored. [...] Read more.
Tonic immobility (TI) serves as an indicator of innate stress response recovery in poultry. Broilers with different TI phenotypes exhibit varying levels of aggressive behavior, which can significantly impact their welfare. However, the influences of TI phenotypes on broiler aggression remain largely unexplored. In this study, broiler chickens were stratified into two distinct phenotypic groups based on the TI duration: short TI (STI) and long TI (LTI). The impacts of TI phenotypes on broiler aggression were investigated by analyzing cecal intestinal morphology, cecal bacteria, plasma metabolites, and corticosterone levels. Compared to LTI broilers, STI broilers showed significantly reduced plasma corticosterone (CORT) levels (p < 0.05) and a decreased frequency of aggressive behaviors, including dominant and subdominant types (p < 0.01). Histological analysis revealed that STI broilers have an increased duodenal villus height and villus-height-to-crypt-depth ratio (p < 0.01), a decreased jejunal crypt depth with an increased villus-height-to-crypt-depth ratio (p < 0.01), and a reduced ileal crypt depth and villus height (p < 0.01) compared to LTI broilers. 16S rDNA sequencing and Linear discriminant analysis effect size (LefSe) identified differential cecal bacterial abundance, notably in the genus cc115 belonging to Firmicutes. Specific microbiota in LTI broilers exhibited significant positive correlations with aggressive behavior and plasma corticosterone, while those in STI broilers showed significant negative correlations. Untargeted plasma metabolomics revealed 21 downregulated and 17 upregulated metabolites between TI phenotypes. Correlation analysis showed that the genus cc115 and 10 plasma metabolites were positively correlated with aggressive behavior, whereas 8 metabolites were negatively correlated. LTI broilers have higher plasma corticosterone content and more intense aggressive behavior than STI broilers. The distinct behavioral and physiological profiles observed in broilers with different TI phenotypes are strongly correlated with their specific gut microbiota and differential plasma metabolite profiles. The identified gut microbial signatures serve as key biomarkers for regulating aggressive behavior in broilers, while the differential plasma metabolites represent potential early indicators for detecting stress and behavioral issues in poultry farming. Full article
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Figure 1
<p>Effects of different TI on the apparent performance of broilers. (<b>A</b>) Timeline of the experiment. (<b>B</b>) TI duration. (<b>C</b>) Plasma corticosterone (mg/mL). (<b>D</b>) Initial weight (kg). (<b>E</b>) Final live weight (kg). (<b>F</b>) Average daily feed intake (g/d). (<b>G</b>) Feed-to-gain ratio (g/g). (<b>H</b>) Aggressive behavior (Freqs/h). (<b>I</b>) Dominant aggressive behavior (Freqs/h). (<b>J</b>) Subdominant aggressive behavior (Freqs/h). All data are shown as the mean ± SEM; # <span class="html-italic">p</span> &lt; 0.10, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n = 10.</p>
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<p>Effects of different TI on the small-intestinal development and morphology of broilers. (<b>A</b>) H&amp;E staining of the duodenum (scale bars = 10 μm). (<b>B</b>) Duodenal crypt depth (mm). (<b>C</b>) Duodenal villus height (mm). (<b>D</b>) Duodenal villus-height-to-crypt-depth ratio. (<b>E</b>) H&amp;E staining of the jejunum (scale bars = 10 μm). (<b>F</b>) Jejunal crypt depth (mm). (<b>G</b>) Jejunal villus height (mm). (<b>H</b>) Jejunal villus-height-to-crypt-depth ratio. (<b>I</b>) H&amp;E staining of the ileum (scale bars = 10 μm). (<b>J</b>) Ileal crypt depth (mm), (<b>K</b>) Ileal villus height (mm). (<b>L</b>) Ileal villus-height-to-crypt-depth ratio. All data are shown as the mean ± SEM; ** <span class="html-italic">p</span> &lt; 0.01, n = 3.</p>
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<p>Effects of different TI on the cecal microbiota of broilers. (<b>A</b>) Cecal content microbial α-diversity. (<b>B</b>) Principal coordinate analysis (PCoA) based on total OTUs. (<b>C</b>) Venn diagram based on the total number of OTUs. (<b>D</b>) Relative abundance levels of cecal microbiota in different TI phenotypes. (<b>E</b>) Specific cecal microbiota of LTI broilers. (<b>F</b>) Specific cecal microbiota of STI broilers. (<b>G</b>) A histogram of LDA scores for differentially abundant taxa. All data are shown as the mean ± SEM; n = 9.</p>
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<p>Effects of different TI on the plasma metabolites of broilers. (<b>A</b>) Principal component analysis (PCA) results of different TI phenotypes. (<b>B</b>) Volcano map showing the differential metabolites between LTI and STI broilers. (<b>C</b>) Significant upregulated differential metabolites. (<b>D</b>) Significant downregulated differential metabolites. (<b>E</b>,<b>F</b>) Partial differential metabolite enrichment pathways. All data are shown as the mean ± SEM; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n = 9.</p>
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<p>Correlation analysis between aggressive behavior, plasma corticosterone, same differential cecal microbiota, and differential plasma metabolites. (<b>A</b>) Correlation network heatmap of aggressive behavior, plasma corticosterone, differential common bacteria, and metabolites. (<b>B</b>) Sankey diagram of aggressive behavior, plasma corticosterone, key bacteria, and metabolites.</p>
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<p>Specific differences correlation analysis in LTI broilers. (<b>A</b>) Heatmap of top 5 specific bacteria in LTI broilers and differential metabolites. (<b>B</b>) Sankey diagram of aggressive behavior, plasma corticosterone, top 5 bacteria, and differential metabolites in LTI broilers. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n = 9.</p>
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<p>Specific differences correlation analysis in STI broilers. (<b>A</b>) Heatmap of top 5 specific microbiota in STI broilers and differential metabolites. (<b>B</b>) Sankey diagram of aggressive behavior, plasma corticosterone, top 5 microbiota, and differential metabolites in STI broilers. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n = 9.</p>
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18 pages, 4222 KiB  
Article
Vertical Stratification Reduces Microbial Network Complexity and Disrupts Nitrogen Balance in Seasonally Frozen Ground at Qinghai Lake in Tibet
by Ni Zhang, Zhiyun Zhou, Yijun Wang, Shijia Zhou, Jing Ma, Jianqing Sun and Kelong Chen
Microorganisms 2025, 13(2), 459; https://doi.org/10.3390/microorganisms13020459 - 19 Feb 2025
Viewed by 195
Abstract
Global climate change has accelerated the reduction of permafrost regions across different altitude gradients, shortening the duration of the freezing period to varying extents. However, the response of the soil microorganisms of frozen soils along altitude gradients remains unclear. In this study, we [...] Read more.
Global climate change has accelerated the reduction of permafrost regions across different altitude gradients, shortening the duration of the freezing period to varying extents. However, the response of the soil microorganisms of frozen soils along altitude gradients remains unclear. In this study, we employed 16S rRNA sequencing and LC-MS metabolomics to investigate the response of soil microbial communities and soil metabolites to vertical stratification in the permafrost soils of the Qinghai Lake region. The results indicated that Proteobacteria, Firmicutes, and Actinobacteria were key soil bacterial phyla in the permafrost soils of Qinghai Lake during the freezing period, with Proteobacteria and Firmicutes showing significant sensitivity to vertical stratification (p < 0.05). The majority of the physicochemical factors exhibited a trend of initially increasing and then decreasing with increasing altitude, whereas pH showed the opposite trend. pH and moisture content were identified as the most important environmental factors influencing soil bacterial community structure. Deterministic processes dominated the assembly of bacterial communities of frozen soils in the Qinghai Lake basin. Co-occurrence network analysis showed that increasing altitude gradients led to a higher average degree of the bacterial network, while reducing network complexity and inter-species connectivity. Soil metabolomics analysis revealed that vertical stratification altered the metabolic profiles of 27 metabolites, with the significantly changed metabolites primarily associated with carbohydrate and amino acid metabolism. In conclusion, the characteristics of the Qinghai Lake permafrost were regulated by regional vertical stratification, which further influenced microbial community structure and soil metabolic characteristics, thereby altering carbon and nitrogen stocks. Specifically, higher altitudes were more favorable for the retention of the carbon and nitrogen stocks of frozen soils in the Qinghai Lake basin. Full article
(This article belongs to the Collection Feature Papers in Environmental Microbiology)
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<p>Sampling point distribution.</p>
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<p>Physicochemical properties of frozen soil samples under vertical stratification in the Qinghai Lake basin. Letters a, b, c, and d indicate significance levels; the same letter denotes no significant difference (<span class="html-italic">p</span> &gt; 0.05), while different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Tem: soil temperature, Moi: soil moisture, TN: total nitrogen, TC: total carbon, pH: soil pH, TP: total phosphorus, TK: total potassium, AN: ammonium nitrogen, NN: nitrate nitrogen, EP: effective phosphorus, AK: available potassium, OM: organic matter.</p>
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<p>Soil bacterial sequencing characteristics under vertical stratification of frozen soils in the Qinghai Lake basin. (<b>a</b>) Sample rarefaction curves; (<b>b</b>) ASV distribution across groups; and (<b>c</b>) alpha diversity indices.</p>
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<p>Bacterial community composition under vertical stratification of frozen soils in the Qinghai Lake basin. (<b>a</b>) Dominant bacterial phyla at the phylum level; (<b>b</b>) dominant bacterial genera at the genus level; (<b>c</b>) differential bacterial phyla at the phylum level; and (<b>d</b>) differential bacterial genera at the genus level. Letters a, b, c, and d indicate statistical significance. Identical letters indicate no significant difference (<span class="html-italic">p</span> &gt; 0.05), while different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Bacterial functional groups under vertical stratification of frozen soils in the Qinghai Lake basin. (<b>a</b>) Major functional groups (Top 10); (<b>b</b>) functional groups with inter-group differences; and (<b>c</b>) carbon and nitrogen cycling-related functional groups and microbial communities. Letters a, b, and c indicate statistical significance. Identical letters indicate no significant difference (<span class="html-italic">p</span> &gt; 0.05), while different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis of frozen soil samples under vertical stratification in the Qinghai Lake basin. (<b>a</b>) Correlation network between bacterial community structure and physicochemical factors. (<b>b</b>) Correlation heatmap between dominant bacterial genera and physicochemical factors. * Indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, *** indicates <span class="html-italic">p</span> &lt; 0.001. Tem: soil temperature, Moi: soil moisture, TN: total nitrogen, TC: total carbon, pH: soil pH, TP: total phosphorus, TK: total potassium, AN: ammonium nitrogen, NN: nitrate nitrogen, EP: effective phosphorus, AK: available potassium, OM: organic matter.</p>
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<p>Factors influencing the bacterial community and community assembly processes of frozen soils in the Qinghai Lake basin. (<b>a</b>) Hierarchical partitioning analysis of factors affecting bacterial community structure; (<b>b</b>) hierarchical partitioning analysis of factors affecting bacterial community diversity; (<b>c</b>) distribution of the βNTI index; and (<b>d</b>) bacterial community assembly processes. Tem: soil temperature, Moi: soil moisture, TN: total nitrogen, TC: total carbon, pH: soil pH, TP: total phosphorus, TK: total potassium, AN: ammonium nitrogen, NN: nitrate nitrogen, EP: effective phosphorus, AK: available potassium, OM: organic matter.</p>
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<p>Heatmap of differential metabolites under vertical stratification of frozen soils in the Qinghai Lake basin.</p>
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<p>Correlation network between bacterial communities and differential metabolites under vertical stratification of frozen soils in the Qinghai Lake basin. Node size represents degree, and node color indicates category, yellow for genus-level bacterial communities and blue for metabolites; edge color indicates correlation type, red for positive correlation and blue for negative correlation.</p>
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<p>Bacterial network patterns (<b>a</b>) and key topological characteristics (<b>b</b>) under vertical stratification of frozen soils in the Qinghai Lake basin. The size of the nodes represents the degree; the node color indicates different modules; the edge color represents positive or negative correlations, with red indicating positive correlations and green indicating negative correlations.</p>
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