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25 pages, 8763 KiB  
Article
Root Microbiome and Metabolome Traits Associated with Improved Post-Harvest Root Storage for Sugar Beet Breeding Lines Under Southern Idaho Conditions
by Rajtilak Majumdar, Shyam L. Kandel, Carl A. Strausbaugh, Anuradha Singh, Suresh Pokhrel and Malick Bill
Int. J. Mol. Sci. 2024, 25(23), 12681; https://doi.org/10.3390/ijms252312681 - 26 Nov 2024
Viewed by 174
Abstract
Post-harvest storage loss in sugar beets due to root rot and respiration can cause >20% sugar loss. Breeding strategies focused on factors contributing to improved post-harvest storage quality are of great importance to prevent losses. Using 16S rRNA and ITS sequencing and sugar [...] Read more.
Post-harvest storage loss in sugar beets due to root rot and respiration can cause >20% sugar loss. Breeding strategies focused on factors contributing to improved post-harvest storage quality are of great importance to prevent losses. Using 16S rRNA and ITS sequencing and sugar beet mutational breeding lines with high disease resistance (R), along with a susceptible (S) commercial cultivar, the role of root microbiome and metabolome in storage performance was investigated. The R lines in general showed higher abundances of bacterial phyla, Patescibacteria at the M time point, and Cyanobacteria and Desulfobacterota at the L time point. Amongst fungal phyla, Basidiomycota (including Athelia) and Ascomycota were predominant in diseased samples. Linear discriminant analysis Effect Size (LEfSe) identified bacterial taxa such as Micrococcales, Micrococcaceae, Bacilli, Glutamicibacter, Nesterenkonia, and Paenarthrobacter as putative biomarkers associated with resistance in the R lines. Further functional enrichment analysis showed a higher abundance of bacteria, such as those related to the super pathway of pyrimidine deoxyribonucleoside degradation, L-tryptophan biosynthesis at M and L, and fungi, such as those associated with the biosynthesis of L-iditol 2-dehydrogenase at L in the R lines. Metabolome analysis of the roots revealed higher enrichment of pathways associated with arginine, proline, alanine, aspartate, and glutamate metabolism at M, in addition to beta-alanine and butanoate metabolism at L in the R lines. Correlation analysis between the microbiome and metabolites indicated that the root’s biochemical composition, such as the presence of nitrogen-containing secondary metabolites, may regulate relative abundances of key microbial candidates contributing to better post-harvest storage. Full article
(This article belongs to the Special Issue Advances and New Perspectives in Plant-Microbe Interactions 2.0)
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Figure 1

Figure 1
<p>Sugar beet genotypes relatively resistant to post-harvest storage diseases showed differences in the abundance of bacterial and fungal phyla and genera compared to the susceptible genotype. Mean relative abundance of (<b>A</b>) bacterial phyla; (<b>B</b>) bacterial genera; (<b>C</b>) fungal phyla; and (<b>D</b>) fungal genera in sugar beet roots at mid and late post-harvest storage stages. Susceptible genotype: Sus_Ck; resistant genotypes: KSG2, KSG3, KSG4, and KSG6; M: mid time point; L: late time point. The data are mean ± standard error of 4 biological replicates, each replicate consists of tissues harvested from 2 sugar beet roots. The heat maps (<b>B</b>,<b>D</b>) are plotted using z-score values of the species abundance. ‘0’ means the abundance is at the mean value. Red color means the species abundance is higher than the mean, and blue color means that the species abundance is lower than the mean. Blue to red transition means abundance of the species is transitioning from ‘lower than mean’ to ‘higher than mean’.</p>
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<p>Beta diversity of bacteria and fungi in the resistant (R: KSG2, 3, 4, and 6) and susceptible (S: Sus_Ck) sugar beet genotypes at mid (M) and late (L) storage time points. (<b>A</b>) Cluster dendrogram of bacterial diversity; (<b>B</b>) principal coordinate analysis (PCoA) of bacterial diversity; (<b>C</b>) comparison of weighted UniFrac distances between S and R genotypes (16S); (<b>D</b>) cluster dendrogram of fungal diversity; (<b>E</b>) principal coordinate analysis (PCoA) of fungal diversity; and (<b>F</b>) comparison of weighted UniFrac distances between S and R genotypes (ITS). The data are mean ± standard error of 4 replicates (each replicate consists of 2 sugar beet roots); * <span class="html-italic">p</span> &lt; 0.05. Solid dark circle next to the treatment represents the susceptible genotype.</p>
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<p>Linear discriminant analysis Effect Size (LEfSe) analysis of sugar beet genotypes at different post-harvest storage time points shows putative biomarkers associated with resistant or susceptible genotypes. (<b>A</b>) Bar plot of bacterial taxa at the mid (M) time point; (<b>B</b>) hierarchal taxonomic cladogram of bacterial taxa at the mid time point; (<b>C</b>) bar plot of bacterial taxa at the late (L) time point; and (<b>D</b>) hierarchal taxonomic cladogram of bacterial taxa at the late time point. Susceptible genotype: Sus_Ck; resistant genotypes: KSG2, KSG3, KSG4, and KSG6. Lowercase letters denote d: domain; p: phylum; c: class; o: order; f: family; g: genus.</p>
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<p>KEGG modules were significantly different (<span class="html-italic">p</span> &lt; 0.05 *) between the resistant and susceptible sugar beet genotypes. (<b>A</b>) Mid (M) storage time point (bacteria; 16S); (<b>B</b>) late (L) storage time point (bacteria; 16S); and (<b>C</b>) mid (M) storage time point (fungi; ITS). Data are mean ± standard error of 4 replicates (each replicate consists of samples obtained from two roots). Sus_Ck: susceptible genotype; KSG2, KSG3, KSG4, and KSG6: resistant genotypes.</p>
Full article ">Figure 4 Cont.
<p>KEGG modules were significantly different (<span class="html-italic">p</span> &lt; 0.05 *) between the resistant and susceptible sugar beet genotypes. (<b>A</b>) Mid (M) storage time point (bacteria; 16S); (<b>B</b>) late (L) storage time point (bacteria; 16S); and (<b>C</b>) mid (M) storage time point (fungi; ITS). Data are mean ± standard error of 4 replicates (each replicate consists of samples obtained from two roots). Sus_Ck: susceptible genotype; KSG2, KSG3, KSG4, and KSG6: resistant genotypes.</p>
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<p>Sparse Correlations for Compositional data (SparCC). (<b>A</b>) Correlation heatmap between bacterial communities; (<b>B</b>) correlation network between bacterial communities; (<b>C</b>) correlation heatmap between fungal communities; and (<b>D</b>) correlation network between fungal communities. A solid line between two bacterial/fungal communities indicates a positive correlation and a dotted line indicates a negative correlation between them. The thicker the solid line, the higher the value of the positive correlation between them.</p>
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<p>Untargeted metabolome analysis of sugar beet roots showed distinct differences between the resistant (R) and susceptible (S) lines at mid and late storage time points. Metabolites showing major differences between the R vs. S lines at the (<b>A</b>) mid storage stage and (<b>B</b>) late storage stage. Sus.Ck: susceptible genotype; KSG2, KSG3, KSG4, and KSG6: relatively resistant genotypes; M: mid and L: late storage time points.</p>
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<p>Pathway enrichment analysis of sugar beet roots. (<b>A</b>) Mid storage time point and (<b>B</b>) late storage time point. Data are mean ± standard error of 4 replicates (each replicate consists of samples obtained from two roots).</p>
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<p>Carbohydrate content in the roots showed small changes between the resistant and susceptible lines at the late storage time point. Cellular contents (mg/g FW) of: (<b>A</b>) sucrose and (<b>B</b>) fructose, glucose and galactose, and raffinose. Data are mean ± standard error of 4 replicates (each replicate consists of samples obtained from two roots); <span class="html-italic">p</span> &lt; 0.05 * between the susceptible (Sus_Ck) and resistant genotypes (KSG2, KSG3, KSG4, and KSG6).</p>
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<p>Correlation analysis between the root microbiome and metabolome at the late storage time point reveals a distinct pattern in the two highly resistant genotypes vs. the susceptible genotype. (<b>A</b>) Susceptible genotype (Sus_Ck); (<b>B</b>) resistant genotype, KSG4; and (<b>C</b>) resistant genotype, KSG6. Data are mean ± standard error of 4 replicates (each replicate consists of samples obtained from two roots). An ‘X’ sign inside the rectangular boxes in the heatmap indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Disease symptoms on sugar beet roots at the late storage time point under indoor storage conditions. Representative samples showing surface coverage with fungal growth in the susceptible genotype (Sus_Ck) and resistant genotypes (KSG2, KSG3, KSG4, and KSG6).</p>
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19 pages, 3904 KiB  
Article
Solid- and Vapor-Phase Antibacterial Activities and Mechanisms of Essential Oils Against Fish Spoilage Bacteria
by Hsuan-Ju Lin, Pang-Hung Hsu, Tze-Chia Lin, Wen-Jung Lu and Hong-Ting Victor Lin
Antibiotics 2024, 13(12), 1137; https://doi.org/10.3390/antibiotics13121137 - 26 Nov 2024
Viewed by 308
Abstract
Essential oils (EOs), regarded as secondary metabolites from plants, possess effective antibacterial properties. This study investigates the antibacterial efficacy of seven citrus EOs against six spoilage bacteria: Vibrio parahaemolyticus, V. harveyi, Photobacterium damselae, Shewanella putrefaciens, Carnobacterium divergens, and [...] Read more.
Essential oils (EOs), regarded as secondary metabolites from plants, possess effective antibacterial properties. This study investigates the antibacterial efficacy of seven citrus EOs against six spoilage bacteria: Vibrio parahaemolyticus, V. harveyi, Photobacterium damselae, Shewanella putrefaciens, Carnobacterium divergens, and Lactobacillus pentosus. The antibacterial activity of these EOs was evaluated using solid- and vapor-phase applications. All tested EOs demonstrated effective antibacterial activity at a concentration of 294 μL/L against Gram-negative bacteria. Notably, lemon and orange EOs exhibited dose-dependent inhibition in both solid- and vapor-phase applications, with minimum effective concentrations ranging from 29.4 to 58.8 μL/L. Following treatment with lemon and orange EOs for 6 h at 1/4 minimum inhibitory concentration, leakage of intracellular DNA and proteins was observed, indicating damage to the cell membrane/wall. Proteomic analysis revealed distinct mechanisms: lemon EO impaired bacterial antioxidant defenses, while orange EO disrupted cell division, leading to reduced bacterial viability. These findings provide valuable insights into the potential of different EO application forms in controlling spoilage bacteria. Full article
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<p>The effect of lemon essential oils in the solid phase (<b>A</b>) and vapor phase (<b>B</b>) on the test organisms at different concentrations (n = 3).</p>
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<p>The effect of orange essential oils in the solid phase (<b>A</b>) and vapor phase (<b>B</b>) on the test organisms at different concentrations (n = 3).</p>
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<p>PCA plot of <span class="html-italic">V. parahaemolyticus</span> proteins treated with different concentrations of lemon EO and orange EO.</p>
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<p>Heatmap of <span class="html-italic">V. parahaemolyticus</span> proteins treated with different concentrations of (<b>A</b>) lemon EO and (<b>B</b>) orange EO.</p>
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<p>Venn diagrams of protein expression (<b>A</b>) upregulation and (<b>B</b>) downregulation in <span class="html-italic">V. parahaemolyticus</span> treated with lemon and orange EOs at different concentrations.</p>
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<p>Volcano plots of protein expression changes in <span class="html-italic">V. parahaemolyticus</span> treated with different concentrations of lemon (<b>A</b>) and orange (<b>B</b>) EO. All the dots represent a protein, and each orange dot represents a DPE.</p>
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<p>Volcano plots of protein expression changes in <span class="html-italic">V. parahaemolyticus</span> treated with different concentrations of lemon (<b>A</b>) and orange (<b>B</b>) EO. All the dots represent a protein, and each orange dot represents a DPE.</p>
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14 pages, 5382 KiB  
Article
Rhizosphere Shifts: Reduced Fungal Diversity and Microbial Community Functionality Enhance Plant Adaptation in Continuous Cropping Systems
by Jichao Li, Yingmei Zuo and Jinyu Zhang
Microorganisms 2024, 12(12), 2420; https://doi.org/10.3390/microorganisms12122420 - 25 Nov 2024
Viewed by 334
Abstract
Continuous cropping problems constitute threats to perennial plant health and survival. Soil conditioners have the potential to enhance plant disease resistance in continuous cropping systems. However, how microbes and metabolites of the rhizosphere respond to soil conditioner addition remains largely unknown, but this [...] Read more.
Continuous cropping problems constitute threats to perennial plant health and survival. Soil conditioners have the potential to enhance plant disease resistance in continuous cropping systems. However, how microbes and metabolites of the rhizosphere respond to soil conditioner addition remains largely unknown, but this knowledge is paramount to providing innovative strategies to enhance plant adaptation in continuous cropping systems. Here, we found that a biochar conditioner significantly improved plant survival rates in a continuous cropping system. The biochar-induced rhizosphere significantly alters the fungal community, causing a decline in fungal diversity and the downregulation of soil microbial community functionality. Specifically, the biochar-induced rhizosphere causes a reduction in the relative abundance of pathogenic Fusarium sp. and phenolic acid concentration, whose variations are the primary causes of continuous cropping problems. Conversely, we observed an unexpected bacterial diversity increase in rhizospheric and non-rhizospheric soils. Our research further identified key microbial taxa in the biochar-induced rhizosphere, namely, Monographella, Acremonium, Geosmithia, and Funneliformis, which enhance soil nutrient availability, suppress Fusarium sp., mitigate soil acidification, and reduce phenolic acid concentrations. Collectively, we highlight the critical role of regular microbial communities and metabolites in determining plant health during continuous cropping and propose a synthetic microbial community framework for further optimizing the ecological functions of the rhizosphere. Full article
(This article belongs to the Special Issue State-of-the-Art Environmental Microbiology in China (2023–2024))
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<p>Materials and methods in experimental design.</p>
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<p>Comparative community structure of fungi and bacteria at the genus level. (<b>A</b>,<b>B</b>) Fungal and bacterial community structure in the rhizosphere with SWTG (biochar), CMHG (grass ash), JGG (corn stover), and CKG (control). The orange boxes indicate the genera with significant alterations. (<b>C</b>,<b>D</b>) Fungal and bacterial community structures in the non-rhizosphere. Similarly, SWTF (biochar), CMHF (grass ash), JGF (corn stover), and CKF (control) are shown.</p>
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<p>Comparison of rhizosphere and non-rhizosphere fungal and bacterial diversity at the genus level. (<b>A</b>,<b>B</b>) A fungal Chao and Shannon index paired map between the rhizosphere and non-rhizosphere. It only shows significant differences between strategy pairs. Higher diversity in the rhizosphere than in the non-rhizosphere is indicated by yellow arrows. Higher diversity in the non-rhizosphere than the rhizosphere is indicated by blue arrows. SWT (biochar), CMH (grass ash), JG (corn stover), and CK (control). (<b>C</b>,<b>D</b>) Bacterial Chao and Shannon index paired map between the rhizosphere and non-rhizosphere. It only shows significant differences between strategy pairs. (<b>E</b>,<b>F</b>) Fungal Chao (<b>i</b>) and Shannon (<b>ii</b>) indices with soil conditioners in the rhizosphere and non-rhizosphere. (<b>G</b>,<b>H</b>) Bacterial Chao (<b>i</b>) and Shannon (<b>ii</b>) indices with soil conditioners in the rhizosphere and non-rhizosphere. *, <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.</p>
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<p>Biochar-induced soil microbial functional abundance variations in the rhizosphere and non-rhizosphere. (<b>A</b>) Hierarchical cluster heatmap of KEGG level 2 functional predictions. (<b>B</b>) Enzyme activity difference cluster heatmap. (<b>C</b>) Protein expression difference cluster heatmap. (<b>D</b>) Metabolic pathway difference cluster heatmap. Soil rhizosphere microbial functions, encompassing KEGG predictions, enzyme activities, protein expression, and metabolic pathways, are downregulated with the reduction in rhizosphere metabolites. CKG (rhizosphere of control ), SWTG (rhizosphere of biochar), CKF (non-rhizosphere of control), and SWTF (non-rhizosphere of biochar).</p>
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<p>Targeted phenolic acid concentration comparison. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>LEfSe analysis and correlation of biomarkers in soil microbiomes. (<b>A</b>) (<b>i</b>–<b>viii</b>). Discriminating fungal and bacterial microorganisms according to LEfSe analysis. In the biochar strategy, the fungal biomarkers <span class="html-italic">Monographella</span>, <span class="html-italic">Acremonium</span>, <span class="html-italic">Geosmithia</span>, and <span class="html-italic">Funneliformis</span> were significantly enriched. (<b>B</b>) Correlation analysis of the Top 20 most abundant fungi. <span class="html-italic">Acremonium</span>, <span class="html-italic">Geosmithia</span>, and <span class="html-italic">Funneliformis</span> exhibit negative correlations with more than 60 percent of the Top 20 most abundant fungi. (<b>C</b>) Histogram of the pathogen abundance of <span class="html-italic">Fusarium</span> sp. A marked decrease in <span class="html-italic">Fusarium</span> sp. is observed in the biochar-amended rhizosphere (SWTG). (<b>D</b>) The interomics correlation network of fungal and bacterial biomarkers. The fungal biomarkers are positively intercorrelated and negatively correlated with bacterial biomarkers. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Network interaction maps between microorganisms, metabolites, and soil nutrients. Biomarker microorganisms were positively correlated with soil nutrients and elevated pH but negatively correlated with phenolic acid (<span class="html-italic">p</span> &lt; 0.001). Both fungal and bacterial biomarker microorganisms were negatively correlated with the pathogen <span class="html-italic">Fusarium</span>. NN, nitrate nitrogen; OM, organic matter; TP, total phosphorus; TN, total nitrogen. *, <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; Blue arrows: negative correlation; red arrows: positive correlation; line thickness indicates correlation strength.</p>
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17 pages, 4799 KiB  
Brief Report
Whole-Genome Sequencing of Peribacillus frigoritolerans Strain d21.2 Isolated in the Republic of Dagestan, Russia
by Maria N. Romanenko, Anton E. Shikov, Iuliia A. Savina, Anton A. Nizhnikov and Kirill S. Antonets
Microorganisms 2024, 12(12), 2410; https://doi.org/10.3390/microorganisms12122410 - 24 Nov 2024
Viewed by 384
Abstract
Pesticide-free agriculture is a fundamental pillar of environmentally friendly agriculture. To this end, there is an active search for new bacterial strains capable of synthesizing secondary metabolites and toxins that protect crops from pathogens and pests. In this study, we isolated a novel [...] Read more.
Pesticide-free agriculture is a fundamental pillar of environmentally friendly agriculture. To this end, there is an active search for new bacterial strains capable of synthesizing secondary metabolites and toxins that protect crops from pathogens and pests. In this study, we isolated a novel strain d21.2 of Peribacillus frigoritolerans from a soil sample collected in the Republic of Dagestan, Russia. Leveraging several bioinformatic approaches on Illumina-based whole-genome assembly, we revealed that the strain harbors certain insecticidal loci (coding for putative homologs of Bmp and Vpa) and also contains multiple BGCs (biosynthetic gene clusters), including paeninodin, koranimine, schizokinen, and fengycin. In total, 21 BGCs were predicted as synthesizing metabolites with bactericidal and/or fungicidal effects. Importantly, by applying a re-scaffolding pipeline, we managed to robustly predict MGEs (mobile genetic elements) associated with BGCs, implying high genetic plasticity. In addition, the d21.2’s genome was free from genes encoding for enteric toxins, implying its safety in use. A comparison with available genomes of the Peribacillus frigoritolerans strain revealed that the strain described here contains more functionally important loci than other members of the species. Therefore, strain d21.2 holds potential for use in agriculture due to the probable manifestation of bactericidal, fungicidal, growth-stimulating, and other useful properties. The assembled genome is available in the NCBI GeneBank under ASM4106054v1. Full article
(This article belongs to the Special Issue Agriculture-Related Microorganisms and Carbon Cycle)
18 pages, 6711 KiB  
Article
Insight into Antifungal Metabolites from Bacillus stercoris 92p Against Banana Cordana Leaf Spot Caused by Neocordana musae
by Qunfang Yu, Pengbo He, Yanxiang Qi, Pengfei He, Ayesha Ahmed, Xin Zhang, He Zhang, Yixin Wu, Shahzad Munir and Yueqiu He
Biomolecules 2024, 14(12), 1495; https://doi.org/10.3390/biom14121495 - 24 Nov 2024
Viewed by 299
Abstract
Banana crop ranks among the most crucial fruit and food crops in tropical and subtropical areas. Despite advancements in production technology, diseases such as cordana leaf spot, caused by Neocordana musae, remain a significant challenge, reducing productivity and quality. Traditional chemical controls [...] Read more.
Banana crop ranks among the most crucial fruit and food crops in tropical and subtropical areas. Despite advancements in production technology, diseases such as cordana leaf spot, caused by Neocordana musae, remain a significant challenge, reducing productivity and quality. Traditional chemical controls are becoming less effective due to the development of resistance in target pathogens, which pose significant environmental and health concerns. Consequently, there is growing attention toward the development of biocontrol strategies. Here, we identified a new bacterial strain, Bacillus stercoris 92p, from the rhizosphere soil of banana. We evaluated its ability to suppress the growth of N. musae and other fungal pathogens that cause leaf spot disease in bananas. The inhibitory effect of B. stercoris 92p were checked using dual culture assays, microscopic observations, and pot experiments. Furthermore, the biocontrol mechanisms were investigated using whole-genome sequencing and biochemical analyses. The results showed that B. stercoris 92p exhibited significant antifungal activity against N. musae and other fungal pathogens, with inhibition rates exceeding 70%. Microscopic examination revealed significant morphological alterations in the hyphae and conidia of the tested pathogens. In pot experiments, B. stercoris 92p effectively reduced the severity of cordana leaf spot, achieving a biocontrol efficacy of 61.55%. Genomic analysis and biochemical tests indicated that B. stercoris 92p produces various antifungal compounds, including lipopeptides (fengycins and surfactins), hydrolytic enzymes (proteases and amylases), and phosphate-solubilizing metabolites. In conclusion, the study highlights that B. stercoris could potentially be used as a potential biological control agent against cordana leaf spot. Full article
(This article belongs to the Special Issue Microbial Biocontrol and Plant-Microbe Interactions)
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<p>(<b>A</b>) Antagonistic activity of Strain 92p against <span class="html-italic">N. musae</span>. The phylogenetic tree of <span class="html-italic">Bacillus</span> sp. Strain 92p was constructed using the maximum-likelihood method for the analysis of two genes: the <span class="html-italic">16S rRNA</span> gene (<b>B</b>) and the <span class="html-italic">gyrB</span> gene sequence (<b>C</b>).</p>
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<p>(<b>A</b>) Antagonistic activity of <span class="html-italic">B. stercoris</span> 92p against phytopathogenic fungi. Antifungal efficacy of <span class="html-italic">B. stercoris</span> 92p against phytopathogenic fungi was evaluated on PDA plates. (<b>B</b>) The graph illustrates the mycelial inhibition (%) by <span class="html-italic">B. stercoris</span> 92p on various phytopathogenic fungi. Bars marked with distinct letters exhibit statistically significant variations (<span class="html-italic">p</span> &lt; 0.05) according to Duncan’s multiple comparison test.</p>
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<p>Morphological changes in fungal pathogen hyphae following treatment with <span class="html-italic">B. stercoris</span> 92p. Arrows highlight alterations, including distortions, dissolution, swelling, deformation, beadlike appearance, and vacuolation of structures induced by <span class="html-italic">B</span>. <span class="html-italic">stercoris</span> 92p. Scale bar: 10 μm.</p>
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<p>Biocontrol effects of <span class="html-italic">B. stercoris</span> 92p against cordana leaf spot of banana. (<b>A</b>) Banana leaves were treated with sterile water, Strain 92p, and carbendazim, respectively. (<b>B</b>) The lesion area of banana leaves after treatment with ddH<sub>2</sub>O, Strain 92p, and carbendazim. Asterisks indicate statistically significant differences according to Duncan’s multiple comparison test (**** <span class="html-italic">p</span> ≤ 0.0001).</p>
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<p>Inhibitory effect of <span class="html-italic">B. stercoris</span> 92p cell-free supernatant on <span class="html-italic">N. musae.</span> (<b>A</b>) PDA media were supplemented with varying concentrations (10%, 20%, and 30% <span class="html-italic">v</span>/<span class="html-italic">v</span>) of the 92p sterile supernatant and inoculated with <span class="html-italic">N. musae</span> mycelial discs. A non-supplemented PDA plate served as the control. (<b>B</b>) Bars marked with distinct letters exhibit statistically significant variations (<span class="html-italic">p</span> ≤ 0.05) according to Duncan’s multiple comparison test.</p>
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<p>The enzymatic activity and the secondary metabolites produced by <span class="html-italic">B. stercoris</span> Strain 92p. (<b>A</b>) Protease production, (<b>B</b>) Amylase production, (<b>C</b>) Phosphate solubilization assay, (<b>D</b>) Cellulase production, (<b>E</b>) β-1,3-Glucanase production, (<b>F</b>) Siderophore production.</p>
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<p>Circular map of <span class="html-italic">B. stercoris</span> 92p genome. The outermost circle visually represents the size and scale of the genome. The default circles from outer to inner correspond to genome size markers, gene information on the forward and reverse strand, non-coding RNA, repetitive elements, GC content, and GC-Skew.</p>
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<p>The heatmap displays the average nucleotide identity among strains 92p and 15 <span class="html-italic">Bacillus</span>. ANI values were computed using FastANI for pairwise genome comparisons, and the heatmap illustrates the percentage of ANI among 15 <span class="html-italic">Bacillus</span> strains, with higher values represented by reddish colors to distinguish strains of the same species.</p>
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<p>MS/MS spectra of surfactin ions. (<b>A</b>): <span class="html-italic">m</span>/<span class="html-italic">z</span> 1008.6574, (<b>B</b>): <span class="html-italic">m</span>/<span class="html-italic">z</span> 1022.6784, (<b>C</b>): <span class="html-italic">m</span>/<span class="html-italic">z</span> 1036.5731.</p>
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<p>The MS/MS analysis of four fengycins (<b>A</b>–<b>D</b>). (<b>A</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 1463.8242, (<b>B</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 1477.8224, (<b>C</b>): <span class="html-italic">m</span>/<span class="html-italic">z</span> 1491.8338, (<b>D</b>) <span class="html-italic">m</span>/<span class="html-italic">z</span> 1505.8638. The black box labeled fingerprint product ions 1080 and 966 indicated that the <span class="html-italic">m</span>/<span class="html-italic">z</span> values 1463.8242 and 1477.8224 correspond to fengycin A. The black box labeled fingerprint product ions 1108 and 994 indicated that the <span class="html-italic">m</span>/<span class="html-italic">z</span> values 1491.8338 and 1505.8638 correspond to fengycin B.</p>
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12 pages, 2124 KiB  
Article
Metabolic Profile of Histomonas meleagridis in Dwyer’s Media with and Without Rice Starch
by Sawsan Ammar, Courtney J. Christopher, Nicole Szafranski, Rebekah Jones, Sree Rajeev, Hector F. Castro, Shawn R. Campagna and Richard Gerhold
Metabolites 2024, 14(12), 650; https://doi.org/10.3390/metabo14120650 - 22 Nov 2024
Viewed by 395
Abstract
Background and objectives: Histomonas meleagridis, the causative agent of histomonosis (i.e., blackhead disease), threatens the poultry industry with serious economic losses due to its high mortality and morbidity in turkey and chicken flocks. In vitro studies are complicated by the inability to [...] Read more.
Background and objectives: Histomonas meleagridis, the causative agent of histomonosis (i.e., blackhead disease), threatens the poultry industry with serious economic losses due to its high mortality and morbidity in turkey and chicken flocks. In vitro studies are complicated by the inability to culture the parasite axenically. Histomonas meleagridis has been propagated in Dwyer’s media, which contains a starch source and serum, for over 50 years. The presence of insoluble starch component in Dwyer’s media represents an obstacle for the commercialization of such media, and the role of starch in media is poorly understood. Methods: To investigate the intracellular metabolomic differences in H. meleagridis and undefined bacteria grown in Dwyer’s media with rice starch (SD) and without rice starch (NR), we conducted a global metabolomics analysis using ultra-high-performance liquid chromatography–high-resolution mass spectrometry. Results: SD significantly supported the growth of H. meleagridis compared to NR. There was no significant difference in bacterial growth between SD and NR media at various timepoints. From the intracellular metabolic analysis of samples collected from the SD and NR media, a total of 170 known metabolites were identified. H. meleagridis appears to be the major contributor to global metabolic differences. Conclusions: We found that riboflavin had the highest variable importance in the projection score, and metabolites involved in riboflavin biosynthesis significantly contributed to the differences between SD and NR in the media immediately after the inoculation of H. meleagridis and undefined bacteria, warranting further investigations into the role of riboflavin biosynthesis in H. meleagridis growth. Full article
(This article belongs to the Section Microbiology and Ecological Metabolomics)
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<p>Growth curve of <span class="html-italic">Histomonas meleagridis</span> grown in Dwyer’s media with (SDM) and without (NR) rice starch. The mean of the log values is represented on the vertical axis and the hours post inoculation (HPI) on the horizontal axis.</p>
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<p>Growth curve of undefined bacteria in <span class="html-italic">Histomonas meleagridis</span> cultures using Dwyer’s media with (SDM) and without (NR) rice starch. The mean of the log values is represented on the vertical axis and the hours post inoculation (HPI) on the horizontal axis. “CFU” stands for colony-forming unit.</p>
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<p>The volcano plots show that the intracellular metabolites are statistically and significantly different between <span class="html-italic">Histomonas meleagridis</span> and undefined bacteria in Dwyer’s media with (SD) and without (NR) rice starch. Fold change equals log<sub>2</sub> (average relative abundance for NR/average relative abundance for SD). Red indicates that the metabolite has higher relative abundance in NR treatment, while blue indicates the metabolite has lower abundance in NR treatment. The metabolites were collected immediately at timepoint t0—representing ~0.25 h post infection—after the inoculation of <span class="html-italic">H. meleagridis</span>.</p>
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<p>Partial least squares discriminant analysis (PLS-DA) of metabolites in Dwyer’s media with (SD) and without (NR) rice inoculated with <span class="html-italic">Histomonas meleagridis</span> and undefined bacterial population at blank and 0 HPI (t0). Ellipse represents 95% confidence interval.</p>
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<p>Variable importance in projection (VIP) scores for the top 15 metabolites contributing the most to the differences in the metabolic profile between Dwyer’s media with and without rice inoculated with <span class="html-italic">Histomonas meleagridis</span> and undefined bacteria in the media blank and 0 HPI. Metabolites with a VIP score over 1 drive the separation in the PLS-DA plot. Riboflavin has the highest VIP score in all 5 components (5.3907-1.1438).</p>
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<p>The pathway analysis reveals that riboflavin metabolism and pathways generating metabolic precursors of riboflavin are significantly altered based on media composition. All metabolites with variable importance in projection (VIP) scores &gt;1 were used to conduct a pathway analysis to identify changes in metabolic pathways altered in NR media compared to SD media. Each circle represents a pathway, and the colors indicate the significance (<span class="html-italic">y</span>-axis), while the size depicts the pathway impact (<span class="html-italic">x</span>-axis). The more intense the shade of red, the lower the <span class="html-italic">p</span>-value, and the bigger the circle, the higher the impact of the pathway. Only pathways with <span class="html-italic">p</span> &lt; 0.05 are labeled.</p>
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<p>Riboflavin and metabolic precursors from interconnected metabolic pathways are altered by the omission of rice starch. G6P: glucose-6-phosphate, TCA: tricarboxylic citric acid, sedoheptulose 1,7P: sedoheptulose 1,7 phosphate, Ru5P: Ribulose 5-Phosphate, Ribo-5P: Ribose 5-phosphate, AMP: adenosine monophosphate, GTP: guanine triphosphate, FMN: flavin mononucleotide, FAD: flavin adenine dinucleotide. The metabolites in italics have increased in levels in the NR media compared to the SD media.</p>
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17 pages, 925 KiB  
Review
Microbiome-Derived Trimethylamine N-Oxide (TMAO) as a Multifaceted Biomarker in Cardiovascular Disease: Challenges and Opportunities
by Kinga Jaworska, Wojciech Kopacz, Mateusz Koper and Marcin Ufnal
Int. J. Mol. Sci. 2024, 25(23), 12511; https://doi.org/10.3390/ijms252312511 - 21 Nov 2024
Viewed by 440
Abstract
Biomarkers play a crucial role in various stages of disease management, including screening, diagnosis, prediction, prognosis, treatment, and safety monitoring. Although they are powerful tools in disease diagnosis, management, and drug development, identifying and validating reliable biomarkers remains a significant challenge. Among potential [...] Read more.
Biomarkers play a crucial role in various stages of disease management, including screening, diagnosis, prediction, prognosis, treatment, and safety monitoring. Although they are powerful tools in disease diagnosis, management, and drug development, identifying and validating reliable biomarkers remains a significant challenge. Among potential microbiome-derived biomarkers, trimethylamine N-oxide (TMAO) has gained notable attention for its link to atherosclerosis and cardiovascular risk. However, despite the growing body of research on TMAO, its practical application in clinical settings for disease management and patient outcome enhancement is still not a reality. This paper presents recent data on the utility of TMAO as a cardiovascular biomarker, categorized by its various roles: diagnostic, prognostic, susceptibility/risk, monitoring, pharmacodynamic/response, predictive, and safety. It also briefly discusses research on TMAO’s potential role in cardiovascular disease development. While TMAO shows promise, particularly in prognostic applications, its reliability as a biomarker has been inconsistent across studies. These variances may result from several confounding factors that affect TMAO plasma levels, including diet, kidney function, and demographic variables. The review aims to elucidate the specific contexts in which TMAO can be valuable, potentially leading to more personalized and effective management of cardiovascular disease. Full article
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<p>TMA/TMAO metaorganismal pathway. TMA—trimethylamine; TMAO—trimethylamine N-oxide; FMO3—flavin-containing monooxygenase 3.</p>
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<p>Hypothetical TMAO effects on cardiovascular pathology. ER stress—endoplasmic reticulum stress; FOXO1—Forkhead box protein O1; IL—interleukin; mt ROS—mitochondrial reactive oxygen species; NF-κB—nuclear factor kappa-light-chain-enhancer of activated B cells; NLRP3—NOD-, LRR-, and pyrin domain-containing protein 3; PERK—protein kinase R-like endoplasmic reticulum kinase; TMAO—trimethylamine N-oxide; TNFα—tumor necrosis factor alpha; VCAM-1—vascular cell adhesion molecule 1.</p>
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21 pages, 1423 KiB  
Article
Analysis of Bacterial Metabolites in Breath Gas of Critically Ill Patients for Diagnosis of Ventilator-Associated Pneumonia—A Proof of Concept Study
by Wojciech Filipiak, Robert Włodarski, Karolina Żuchowska, Alicja Tracewska, Magdalena Winiarek, Dawid Daszkiewicz, Marta Marszałek, Dagmara Depka and Tomasz Bogiel
Biomolecules 2024, 14(12), 1480; https://doi.org/10.3390/biom14121480 - 21 Nov 2024
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Abstract
Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated patients and may develop into life-threatening conditions. Yet, existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administered prophylactically. This study aims to evaluate [...] Read more.
Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated patients and may develop into life-threatening conditions. Yet, existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administered prophylactically. This study aims to evaluate the suitability of volatile bacterial metabolites for the breath-based test, which is used for diagnosing Ventilator-Associated Pneumonia (VAP). The in vitro experiments with pathogenic bacteria most prevalent in VAP etiology (i.e., Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were performed to identify bacteria-derived metabolites using a specially designed cultivation system enabling headspace sampling for GC-MS analysis. Thirty-nine compounds were found to be significantly metabolized by tested species and, therefore, selected for monitoring in the exhaled breath of critically ill, mechanically ventilated (MV) patients. The emission of volatiles from medical respiratory devices was investigated to estimate the risk of spoiling breath results with exogenous pollutants. Bacterial metabolites were then evaluated to differentiate VAP patients from non-infected MV controls using Receiver Operating Characteristic (ROC) analysis, with AUC, sensitivity, and specificity calculated. Nine bacterial metabolites that passed verification through a non-parametric ANOVA test for significance and LASSO penalization were identified as key discriminators between VAP and non-VAP patients. The diagnostic model achieved an AUC of 0.893, with sensitivity and specificity values of 87% and 82.4%, respectively, being competitive with traditional methods. Further validation could solidify its clinical utility in critical care settings. Full article
(This article belongs to the Section Molecular Biomarkers)
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<p>Growth curves of bacteria cultivated in vitro: (<b>A</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>B</b>) <span class="html-italic">Escherichia coli</span>, (<b>C</b>) <span class="html-italic">Klebsiella pneumonia</span>, and (<b>D</b>) <span class="html-italic">Pseudomonas aeruginosa</span>. Colony-Forming Units (CFU/mL) are plotted after logarithmic transformation in the function of incubation time.</p>
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<p>Comparison of time-dependent profiles for production of <b>ethyl acetate</b> from (<b>A</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>B</b>) <span class="html-italic">Escherichia coli</span>, (<b>C</b>) <span class="html-italic">Klebsiella pneumoniae</span>, and (<b>D</b>) <span class="html-italic">Pseudomonas aeruginosa</span>.</p>
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<p>Comparison of time-dependent profiles for production of dimethyl sulfide from (<b>A</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>B</b>) <span class="html-italic">Escherichia coli</span>, (<b>C</b>) <span class="html-italic">Klebsiella pneumoniae</span>, and (<b>D</b>) <span class="html-italic">Pseudomonas aeruginosa</span>.</p>
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<p>Emission of VOCs from the medical respiratory device parts (endotracheal tube with disposable catheter mount) for exemplary VOCs: (<b>A</b>) p-xylene, (<b>B</b>) ethyl acetate, (<b>C</b>) 1-butanol, and (<b>D</b>) dimethyl sulfide.</p>
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<p>Performance of the Receiver Operating Characteristic (ROC) model composed of nine bacterial metabolites (ethyl acetate, 3-methyl-1-butanol, <span class="html-italic">n</span>-heptane, dimethyl disulfide, decanal, 1-butanol, ethyl methyl sulfide, dimethyl sulfide, and methacrolein) for discrimination of breath samples of VAP patients and uninfected controls. AUC: Area Under Curve; CI: Confidence Intervals; TP: True Positive; TN: True Negative; FP: False positive; FN: False Negative; Sens.: Sensitivity = TP/(TP+FN); Spec.: Specificity = TN/(TN + FP); Acc.: Accuracy = (TP + TN)/(P + N); FDR: False Discovery Rate = FP/(FP + TP).</p>
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19 pages, 6575 KiB  
Article
Chronic Sleep Deprivation Causes Anxiety, Depression and Impaired Gut Barrier in Female Mice—Correlation Analysis from Fecal Microbiome and Metabolome
by Lingyue Li, Zilin Meng, Yuebing Huang, Luyao Xu, Qianling Chen, Dongfang Qiao and Xia Yue
Biomedicines 2024, 12(12), 2654; https://doi.org/10.3390/biomedicines12122654 - 21 Nov 2024
Viewed by 293
Abstract
Background: Chronic sleep deprivation (CSD) plays an important role in mood disorders. However, the changes in the gut microbiota and metabolites associated with CSD-induced anxiety/depression-like behavior in female mice have not been determined. Due to the influence of endogenous hormone levels, females are [...] Read more.
Background: Chronic sleep deprivation (CSD) plays an important role in mood disorders. However, the changes in the gut microbiota and metabolites associated with CSD-induced anxiety/depression-like behavior in female mice have not been determined. Due to the influence of endogenous hormone levels, females are more susceptible than males to negative emotions caused by sleep deprivation. Here, we aim to investigate how CSD changes the gut microbiota and behavior and uncover the relationship between CSD and gut microbiota and its metabolites in female mice. Methods: We used a 48-day sleep deprivation (SD) model using the modified multiple platform method (MMPM) to induce anxiety/depression-like behavior in female C57BL/6J mice and verified our results using the open field test, elevated plus maze, novel object recognition test, forced swim test, and tail suspension test. We collected fecal samples of mice for 16S rDNA sequencing and untargeted metabolomic analysis and colons for histopathological observation. We used Spearmen analysis to find the correlations between differential bacterial taxa, fecal metabolites, and behaviors. Results: Our study demonstrates that CSD induced anxiety/depressive-like behaviors in female mice. The results of 16S rDNA sequencing suggested that the relative abundance of the harmful bacteria g_ Rothia, g_ Streptococcus, g_ Pantoea, and g_ Klebsiella were significantly increased, while the beneficial bacteria g_ Rikenella, g_ Eubacterium]-xylanophilum-group, and g_ Eisenbergiella were significantly decreased after SD. Glycerophospholipid metabolism and glutathione metabolism were identified as key pathways in the fecal metabolism related to oxidative stress and inflammatory states of the intestine. Histological observation showed hyperplasia of epithelial cells, a decrease in goblet cells, and glandular atrophy of the colon in SD mice. There were correlations between some of the differential bacterial taxa, fecal metabolites, and behaviors. Conclusion: In summary, we found that CSD induced anxiety/depression-like behavior, caused gut microbiota dysbiosis, altered fecal metabolism, and damaged the colon barrier in female mice. Full article
(This article belongs to the Section Endocrinology and Metabolism Research)
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<p>SD procedure and behavior test. (<b>A</b>) Schematic design of 48-d SD procedure and behavior test. (<b>B</b>) Diagram of the MMPM. (<b>C</b>) Representative tracking plot from the OFT. (<b>D</b>–<b>F</b>) Total distance (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4/5 per group), central square duration (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4 per group), and the number of entries in the center (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group) during the OFT. (<b>G</b>) Representative track plot of the EPM test. (<b>H</b>–<b>J</b>) Time spent in the open arms (unpaired <span class="html-italic">t</span>-test), the number of entries in the open arms (unpaired <span class="html-italic">t</span>-test), and the anxiety index during the EPM test (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). (<b>K</b>) Diagram of the NORT. The green polyhedron represents familiar object, the red cube represents the novel object. (<b>L</b>) Recognition index of NORT (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 4 per group). (<b>M</b>) Diagram of the FST. (<b>N</b>) Immobility time during FST (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). (<b>O</b>) Diagram of the TST. (<b>P</b>) Immobility time during TST (unpaired <span class="html-italic">t</span>-test, <span class="html-italic">n</span> = 5 per group). All data are presented as mean ± SEM. * <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.</p>
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<p>Colon pathological analysis. (<b>A</b>) Hematoxylin and eosin (H&amp;E) staining. Bar = 300 μm and 60 μm. (<b>B</b>) Scores of histological changes in H&amp;E staining (<span class="html-italic">n</span> = 3). (<b>C</b>) Alcian Blue Periodic Acid Schiff (AB-PAS) Staining. Bar = 300 μm and 200 μm. (<b>D</b>) Goblet cell counting of AB-PAS staining (<span class="html-italic">n</span> = 3). All data are presented as mean ± SEM. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Fecal microbiome data analysis after SD. (<b>A</b>) Venn diagram. (<b>B</b>,<b>C</b>) In representative diagrams of alpha diversity, all alpha diversity indicators have no statistically significant differences. (<b>D</b>) Principal coordinates analysis (PcoA) plot using Bray–Curtis distance. (<b>E</b>) The ratio of relative abundances of phylum level. (<b>F</b>) The ratio of relative abundances of family level. (<b>G</b>) The ratio of relative abundances of genus level. (<b>H</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the phylum level. (<b>I</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the family level. (<b>J</b>) The top 10 species with a <span class="html-italic">p</span>-value less than 0.05 at the genus level.</p>
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<p>Fecal metabolomics after SD. (<b>A</b>) Score plot of PLS-DA model in positive ion model. (<b>B</b>) Permutation plot in positive ion model. (<b>C</b>) Heatmap graph of differential metabolites in positive ion model, the metabolites are clustered according to the similarity of the metabolite expression profiles. (<b>D</b>) Volcano plot in positive ion model, showing the distribution of differential metabolites. (<b>E</b>–<b>H</b>) Score plot of PLS-DA, permutation plot, heatmap graph, and volcano plot in negative ion model. (<b>I</b>) Differential metabolite statistics. (<b>J</b>,<b>K</b>) KEGG enrichment analysis of differential metabolites in positive ion model and negative ion model.</p>
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<p>Correlation analysis between differential genera, metabolites, and behavioral indicators. (<b>A</b>,<b>B</b>) Correlation heatmap and correlation network between the top 10 genera with <span class="html-italic">p</span>-values less than 0.05 and metabolites in glycerophospholipid metabolism pathway and glutathione metabolism. (<b>C</b>) Correlation heatmap between the top 10 genera with <span class="html-italic">p</span>-values less than 0.05 and behavioral indicators. (<b>D</b>) Correlation heatmap between metabolites in glycerophospholipid metabolism pathway and glutathione metabolism and behavioral indicators. All data are presented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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15 pages, 5548 KiB  
Article
Valorization of Fruit By-Products Through Lactic Acid Fermentation for Innovative Beverage Formulation: Microbiological and Physiochemical Effects
by Elisabetta Chiarini, Valentina Alessandria, Davide Buzzanca, Manuela Giordano, Negin Seif Zadeh, Francesco Mancuso and Giuseppe Zeppa
Foods 2024, 13(23), 3715; https://doi.org/10.3390/foods13233715 - 21 Nov 2024
Viewed by 442
Abstract
The increase in food production is accompanied by an increase in waste, particularly agricultural by-products from cultivation and processing. These residues are referred to as agricultural by-products. To address this issue, biotechnological processes can be used to create new applications for these by-products. [...] Read more.
The increase in food production is accompanied by an increase in waste, particularly agricultural by-products from cultivation and processing. These residues are referred to as agricultural by-products. To address this issue, biotechnological processes can be used to create new applications for these by-products. This study explored the use of LAB strains (Lactiplantibacillus plantarum, Streptococcus thermophilus, Lactobacillus delbrueckii subsp. bulgaricus, and Limosilactobacillus fermentum) on by-products such as white grape pomace, cocoa bean shells, apple pomace, and defatted roasted hazelnut to develop yoghurt-style fruit beverages. Microbial load and pH changes were monitored during a 24 h fermentation and 14-day shelf life at 5 °C. Concentrations of sugars, organic acids, and volatile organic compounds were also analyzed using HPLC and GC-qMS. The results showed that optimizing the matrix led to significant bacterial growth, with viable microbes remaining under refrigeration. In particular, the strain of L. plantarum tested on the cocoa bean shell yielded the most promising results. After 24 h of fermentation, the strain reached a charge of 9.3 Log CFU/mL, acidifying the substrate to 3.9 and producing 19.00 g/100 g of lactic acid. Aromatic compounds were produced in all trials, without off-flavours, and characteristic fermented food flavours developed. Additionally, secondary metabolites produced by lactic acid bacteria may enhance the health benefits of these beverages. Full article
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<p>Evolution of the bacteria count (Log CFU/mL; <b>left</b>) and pH (<b>right</b>) of the LAB strains in <span class="html-italic">Moscato</span> grape pomace (MP), cocoa bean shells (CBSs), apple pomace (AP) and de-fatted roasted hazelnut (DH) during 24 h of incubation at 37 °C and 14 days at 5 °C. Bars represent statistical variances in triplicate measurements of duplicate fermentations (Dunn’s test, Bonferroni, <span class="html-italic">p</span> &gt; 0.025).</p>
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<p>PCoA on data dispersion produced on the multivariate distance matrix from sugars and organic acids detected by HPLC analysis. For each fruit by-product, the results obtained for each strain are reported. For each matrix, the initial condition (T0) was compared to the fermented beverage after 24 h.</p>
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<p>PCoA on data dispersion produced on the multivariate distance matrix from VOCs. For each fruit by-product, the results obtained for each strain are reported. For each matrix, the initial condition (T0) is compared to the fermented beverage after 24 h.</p>
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<p>Non-metric multidimensional scaling (NMDS) on data dispersion on VOCs. For each fruit by-product, the results obtained for each strain are reported. The main volatile compounds produced during each fermentation are highlighted.</p>
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29 pages, 829 KiB  
Review
Targeting the Gut Microbiota for Prevention and Management of Type 2 Diabetes
by Sabrina Donati Zeppa, Marco Gervasi, Alessia Bartolacci, Fabio Ferrini, Antonino Patti, Piero Sestili, Vilberto Stocchi and Deborah Agostini
Nutrients 2024, 16(22), 3951; https://doi.org/10.3390/nu16223951 - 19 Nov 2024
Viewed by 381
Abstract
Type 2 diabetes (T2D) is a chronic metabolic disorder with a heterogeneous etiology encompassing societal and behavioral risk factors in addition to genetic and environmental susceptibility. The cardiovascular consequences of diabetes account for more than two-thirds of mortality among people with T2D. Not [...] Read more.
Type 2 diabetes (T2D) is a chronic metabolic disorder with a heterogeneous etiology encompassing societal and behavioral risk factors in addition to genetic and environmental susceptibility. The cardiovascular consequences of diabetes account for more than two-thirds of mortality among people with T2D. Not only does T2D shorten life expectancy, but it also lowers quality of life and is associated with extremely high health expenditures since diabetic complications raise both direct and indirect healthcare costs. An increasing body of research indicates a connection between T2D and gut microbial traits, as numerous alterations in the intestinal microorganisms have been noted in pre-diabetic and diabetic individuals. These include pro-inflammatory bacterial patterns, increased intestinal permeability, endotoxemia, and hyperglycemia-favoring conditions, such as the alteration of glucagon-like peptide-1 (GLP-1) secretion. Restoring microbial homeostasis can be very beneficial for preventing and co-treating T2D and improving antidiabetic therapy outcomes. This review summarizes the characteristics of a “diabetic” microbiota and the metabolites produced by microbial species that can worsen or ameliorate T2D risk and progression, suggesting gut microbiota-targeted strategies to restore eubiosis and regulate blood glucose. Nutritional supplementation, diet, and physical exercise are known to play important roles in T2D, and here their effects on the gut microbiota are discussed, suggesting non-pharmacological approaches that can greatly help in diabetes management and highlighting the importance of tailoring treatments to individual needs. Full article
(This article belongs to the Special Issue Dietary Habit, Gut Microbiome and Human Health)
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<p>An integrated approach to maintaining a healthy gut microbiota for T2D prevention and management.</p>
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15 pages, 3023 KiB  
Article
Energy Metabolite, Immunity, Antioxidant Capacity, and Rumen Microbiota Differences Between Ewes in Late Gestation Carrying Single, Twin, and Triplet Fetuses
by Jiaxin Chen, Chunhui Duan, Sicong Yue, Xiaona Liu, Jinhui Li, Yingjie Zhang and Yueqin Liu
Animals 2024, 14(22), 3326; https://doi.org/10.3390/ani14223326 - 19 Nov 2024
Viewed by 381
Abstract
The objective of this study was to investigate the differences in the energy metabolites, immunity, antioxidant capacity, and rumen microbiota of ewes with different numbers of fetuses. Thirty healthy ewes were selected and divided into single- (SL, n = 10), twin- (TL, n [...] Read more.
The objective of this study was to investigate the differences in the energy metabolites, immunity, antioxidant capacity, and rumen microbiota of ewes with different numbers of fetuses. Thirty healthy ewes were selected and divided into single- (SL, n = 10), twin- (TL, n = 10), and triplet-fetal (PL, n = 10) ewes according to the number of fetuses. Sampling was carried out on days 21 (Q21) and 7 (Q7) before lambing. The results show no differences (p > 0.05) in the DMI and BW of ewes with different numbers of fetuses, and the body condition score (BCS) of PL ewes was lower (p < 0.05) than that of SL ewes. The concentrations of β-hydroxybutyric acid (BHBA), non-esterified fatty acids (NEFA), interleukin-2 (IL-2), interleukin-6 (IL-6), and tumor necrosis factor α (TNF-α) in the PL ewes were higher (p < 0.05), while the glucose (Glu), triglyceride (TG), total cholesterol (TC), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and total antioxidant capacity (T-AOC) values were lower (p < 0.05) than those of the SL ewes. ANOSIM analysis showed that the rumen bacterial structure of the SL, TL, and PL ewes was different on days Q21 and Q7. The relative abundance of Firmicutes and Bacteroidota in the rumen was affected (p < 0.05) by the number of fetuses: the relative abundance of Firmicutes (Ruminococcus, Butyrivibrio, Christensenellaceae_R-7_group, Lachnospiraceae_AC2044_group, Lachnospiraceae_XPB1014_group, and Anaeroplasma) was higher (p < 0.05), while that of Bacteroidota (Prevotella, Prevotellaceae_UCG-003, and Prevotellaceae_UCG-001) was lower (p < 0.05) in the SL ewes than in the PL ewes. In summary, the rumen microbial structure and energy metabolites of ewes in late gestation with different numbers of fetuses were different. Triplet-fetal ewes were characterized by lower BCS and antioxidant capacity and were prone to the triggering of inflammatory responses. Full article
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<p>Rumen microbiota of ewes in late gestation with different numbers of fetuses. (<b>A</b>) The rarefaction curve of ewes with different numbers of fetuses in late gestation. (<b>B</b>) Chao1 index and Shannon index (<b>C</b>) for the rumen. Principal coordinate analysis (PCoA) of rumen microbiota from ewes with different numbers of fetuses on days Q21 (<b>D</b>) and Q7 (<b>E</b>).</p>
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<p>Stacked bar graphs of relative abundance of rumen microbiota at the phylum and genus levels from ewes in late gestation with different numbers of fetuses. (<b>A</b>) Stacked bar graphs of the average relative abundances of the phyla of ewes in late gestation with different numbers of fetuses. (<b>B</b>) Stacked bar graphs of the relative abundances of genera of ewes in late gestation with different numbers of fetuses.</p>
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13 pages, 3705 KiB  
Article
Effect of TetR Family Transcriptional Regulator PccD on Phytosterol Metabolism of Mycolicibacterium
by Peiyao Xiao, Delong Pan, Fuyi Li, Yuying Liu, Yang Huang, Xiuling Zhou and Yang Zhang
Microorganisms 2024, 12(11), 2349; https://doi.org/10.3390/microorganisms12112349 - 18 Nov 2024
Viewed by 472
Abstract
Androstenedione (AD) is an important intermediate for the production of steroidal drugs. The process of transforming phytosterols into AD by Mycolicibacterium is mainly the degradation process of the phytosterol side chain, and the excessive accumulation of propionyl-CoA produced by Mycobacterium will produce toxic [...] Read more.
Androstenedione (AD) is an important intermediate for the production of steroidal drugs. The process of transforming phytosterols into AD by Mycolicibacterium is mainly the degradation process of the phytosterol side chain, and the excessive accumulation of propionyl-CoA produced by Mycobacterium will produce toxic effects, which seriously restricts the transformation performance of strains. In this study, Mycolicibacterium sp. LZ2 (Msp) was used as the research object to study the transcription factor PccD of the TetR family, which has the role of propionyl-CoA metabolism regulation. By constructing overexpression and deletion strains of pccD, it was confirmed that pccD had an inhibitory effect on the transcription of propionyl-CoA carboxylase genes (pccA and pccB). Electrophoretic Mobility Shift Assay (EMSA) and DNase I footprint analysis demonstrated that PccD is directly involved in the transcriptional regulation of pccA and pccB and is a negative transcriptional regulator of the pcc operon. In the study of phytosterol transformation, the growth rate and bacterial viability of Msp-ΔpccD were higher than Msp, but the growth of Msp-pccD was inhibited. As a result of testing of intracellular propionyl-CoA levels and AD production yields, it was found that lower propionyl-CoA levels and higher AD production yields were observed in Msp-ΔpccD. The results expand the cognition of propionyl-CoA metabolism regulation and provide a theoretical basis and reference for the rational transformation of phytosterol transformation strains and secondary metabolite synthesis strains with propionyl-CoA as a substrate, which has important research significance. Full article
(This article belongs to the Special Issue Microbial Metabolic Engineering Technology)
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<p>Genomic context of the PCC pathway gene cluster in mycobacteria and their close relatives. Grey shades represent conserved regions between genomes, and grey levels represent the Identity of adjacent genes, whose values are displayed in the shadows.</p>
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<p>Gene expression levels of <span class="html-italic">pccA</span> (<b>a</b>), <span class="html-italic">pccB</span> (<b>b</b>), and <span class="html-italic">pccD</span> (<b>c</b>) in Msp, Msp-<span class="html-italic">pccD</span>, and Msp-Δ<span class="html-italic">pccD</span> detected by qRT-PCR. These values are the mean of the standard deviations of three replicate experiments. ****, <span class="html-italic">p</span> &lt; 0.0001 (unpaired <span class="html-italic">t</span>-test).</p>
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<p>Transcription factor PccD binds to upstream promoter regions of <span class="html-italic">pccA</span> and <span class="html-italic">pccB</span> genes in Msp. (<b>a</b>) Genetic organization of the <span class="html-italic">pcc</span> operon in the Msp. (<b>b</b>) EMSA of His-PccD protein with upstream promoter regions of <span class="html-italic">pccA</span> and <span class="html-italic">pccB</span>. (<b>c</b>) Electropherograms of a DNase I digest of <span class="html-italic">pccA</span> and <span class="html-italic">pccB</span> promoter probe incubated with 2 μg of His-PccD.</p>
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<p>PccD negatively regulates the metabolism of propionyl-CoA. (<b>a</b>) Growth curves of strains Msp, Msp-<span class="html-italic">pccD</span> and Msp-Δ<span class="html-italic">pccD</span> on phytosterol medium. (<b>b</b>) Cell viability of strains Msp, Msp-<span class="html-italic">pccD</span> and Msp-Δ<span class="html-italic">pccD</span> on phytosterol medium. (<b>c</b>) Intracellular propionyl-CoA concentrations of strains Msp, Msp-<span class="html-italic">pccD</span>, and Msp-Δ<span class="html-italic">pccD</span> were cultured in a phytosterol medium for 72 h and 120 h. The error bars represent the standard deviation of the three biological replicates. NS <span class="html-italic">p</span> &gt; 0.05, *** <span class="html-italic">p</span> ≤ 0.001. (ANOVA analysis).</p>
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<p>Yield of transformation of phytosterols into AD by strains Msp, Msp-<span class="html-italic">pccD</span> and Msp-Δ<span class="html-italic">pccD</span>. The error bars represent the standard deviation of the three biological replicates.</p>
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<p>The regulatory mechanism model of transcription factor PccD regulating MMC pathway.</p>
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20 pages, 10468 KiB  
Article
Characterization of a Bacterium Isolated from Hydrolyzed Instant Sea Cucumber Apostichopus japonicus Using Whole-Genome Sequencing and Metabolomics
by Xin Luo, Zhixuan Zhang, Zhangyi Zheng, Wenwen Zhang, Tinghong Ming, Lefei Jiao, Xiurong Su, Jiajie Xu and Fei Kong
Foods 2024, 13(22), 3662; https://doi.org/10.3390/foods13223662 - 17 Nov 2024
Viewed by 457
Abstract
Autolysis in the sea cucumber Apostichopus japonicus is typically triggered by degradation caused by microorganisms within their bodies. However, information on this topic remains limited. Recently, we isolated and purified a bacterial strain from hydrolyzed instant sea cucumber samples. To investigate its potential [...] Read more.
Autolysis in the sea cucumber Apostichopus japonicus is typically triggered by degradation caused by microorganisms within their bodies. However, information on this topic remains limited. Recently, we isolated and purified a bacterial strain from hydrolyzed instant sea cucumber samples. To investigate its potential role in the autolysis process, this study employed whole-genome sequencing and metabolomics to explore its genetic and metabolic characteristics. The identified strain was classified as Lysinibacillus xylanilyticus and designated with the number XL-2024. Its genome size is 5,075,210 bp with a GC content of 37.33%, encoding 5275 genes. Functional database comparisons revealed that the protein-coding genes were distributed among glucose metabolism hydrolase, metal hydrolase, lysozyme, cell wall hydrolase, and CAZymes. Compared to 20 closely related strains, L. xylanilyticus XL-2024 shared 1502 core homologous genes and had 707 specific genes. These specific genes were mainly involved in the carbohydrate metabolism pathway and exhibited glycosyl bond hydrolase activity. Metabolomic analysis showed that L. xlanilyticus XL-2024 produced several metabolites related to polysaccharide degradation, including peptidase, glucanase, and pectinase. Additionally, the presence of antibacterial metabolites such as propionic acid and ginkgo acid among its metabolites may enhance the stability of the sea cucumber hydrolysate. In summary, L. xylanilyticus XL-2024 may play a pivotal role in the autolysis of A. japonicus. The results of this study provide a strong foundation for understanding how to prevent autolysis in A. japonicus and for better utilizing L. xylanilyticus XL-2024. Full article
(This article belongs to the Section Foods of Marine Origin)
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<p>Percentage (%) of genes functionally annotated in various databases within the <span class="html-italic">L. xylanilyticus</span> XL-2024 genome.</p>
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<p>Annotation and functional classification of genes in NR (<b>a</b>), COG (<b>b</b>), GO (<b>c</b>), and KEGG (<b>d</b>) databases within the <span class="html-italic">L. xylanilyticus</span> XL-2024 genome.</p>
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<p>Pan analysis of the genome of <span class="html-italic">L. xylanilyticus</span> XL-2024 and 20 other similar-type strains. (<b>a</b>) Flower plot displaying the core and specific proteins in 21 strains. Each petal represents a strain, with the number of core proteins shown in the center. The non-overlapping sections indicate the number of strain-specific proteins, and the strain name is next to each petal. The <span class="html-italic">L. xylanilyticus</span> (XL-2024) is highlighted in red. (<b>b</b>) COG function classification of <span class="html-italic">L. xylanilyticus</span> XL-2024-specific genes. (<b>c</b>) KEGG pathway classification of <span class="html-italic">L. xylanilyticus</span> XL-2024-specific genes. (<b>d</b>) GO function classification of <span class="html-italic">L. xylanilyticus</span> XL-2024-specific genes.</p>
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<p>Metabolite analysis of <span class="html-italic">L. xylanilyticus</span> XL-2024 in the positive ion mode (<b>a</b>) and negative ion mode (<b>b</b>), respectively.</p>
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<p>KEGG functional annotation analysis of secondary metabolites of <span class="html-italic">L. xylanilyticus</span> XL-2024. (<b>a</b>) KEGG pathway classification diagram in the positive ion mode. (<b>b</b>) KEGG pathway classification diagram in the negative ion mode. (<b>c</b>) Top 20 KEGG pathway classification diagram in the positive ion mode. (<b>d</b>) Top 20 KEGG pathway classification diagram in the negative ion mode.</p>
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13 pages, 5675 KiB  
Article
Protective Role of Indole-3-Acetic Acid Against Salmonella Typhimurium: Inflammation Moderation and Intestinal Microbiota Restoration
by Yuxin Fan, Qinglong Song, Siyu Li, Jiayu Tu, Fengjuan Yang, Xiangfang Zeng, Haitao Yu, Shiyan Qiao and Gang Wang
Microorganisms 2024, 12(11), 2342; https://doi.org/10.3390/microorganisms12112342 - 16 Nov 2024
Viewed by 462
Abstract
Indole-3-acetic acid (IAA), a metabolite derived from microbial tryptophan metabolism, plays a crucial role in regulating intestinal homeostasis. However, the influence and potential applications of IAA in the context of animal pathogen infections remain underexplored. This study investigates the prophylactic effects of IAA [...] Read more.
Indole-3-acetic acid (IAA), a metabolite derived from microbial tryptophan metabolism, plays a crucial role in regulating intestinal homeostasis. However, the influence and potential applications of IAA in the context of animal pathogen infections remain underexplored. This study investigates the prophylactic effects of IAA pretreatment against Salmonella typhimurium (ST) SL1344 infection, focusing on its ability to attenuate inflammatory responses, enhance intestinal barrier integrity, inhibit bacterial colonization, and restore colonic microbiota dysbiosis. The results demonstrated that IAA ameliorated the clinical symptoms in mice, as evidenced by reduced weight loss and histopathological damage. Furthermore, IAA inhibited the inflammatory response by downregulating the gene expression of pro-inflammatory cytokines IL-17A, TNF-α, IL-1β, and IL-6 in colon, ileum, and liver. IAA also preserved the integrity of the intestinal mucosal barrier and promoted the expression of tight junction proteins. Additionally, 16S rRNA gene sequencing revealed significant alterations in intestinal microbiota structure induced by ST infection following IAA treatment. Notable changes in β diversity and species richness were characterized by the enrichment of beneficial bacteria including Bacteroideaceae, Spirillaceae, and Bacillus. The proliferation of Salmonella enterica subspecies enterica serovar Typhi was significantly inhibited, thereby enhancing the intestinal health of the host. In summary, the oral administration of IAA contributes to the alleviation of inflammation, restoration of the intestinal barrier, and correction of colonic microbiota disturbance in mice challenged with ST. Full article
(This article belongs to the Section Gut Microbiota)
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<p>Effects of IAA supplementation on growth performance and survival rate of mice. Body weight change of mice after ST-infected 1, 2, 3, 4 days (<b>A</b>). Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 8, * <span class="html-italic">p</span> &lt; 0.05. Compared with CON, the survival rate of mice in ST_IAA_40, ST_IAA_80, ST_IAA_160, and ST_CON after ST-infected 4 days (<b>B</b>), <span class="html-italic">n</span> = 10.</p>
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<p>Effects of IAA supplementation on the histopathology of colon and ileum of mice. Summary of histopathological scores of colon (<b>A</b>) and ileum (<b>C</b>); inflammatory cell infiltration, lamina propria edema, acinar dilation, goblet cells decrease and fibrosis scores of colon (<b>B</b>) and ileum (<b>D</b>) tissue; histopathological changes of colon (×40, ×100, ×200; yellow arrows represent inflammatory cell infiltration caused by ST) (<b>E</b>). Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 8, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of IAA supplementation on intestinal and tissue inflammation of mice. Relative mRNA expression levels of inflammatory cytokines <span class="html-italic">TNF-α</span>, <span class="html-italic">IL-1β</span>, and <span class="html-italic">IL-6</span> in colon (<b>A</b>–<b>C</b>), ileum (<b>D</b>–<b>F</b>) and liver (<b>G</b>–<b>I</b>). Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 7–8, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of IAA supplementation on intestinal and liver tight junction protein expression of mice. mRNA expression levels of <span class="html-italic">Occludin</span> in colon (<b>A</b>), ileum (<b>B</b>), and liver (<b>C</b>). Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 7–8, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of IAA on transcription of intestinal Th17 and ILC3 of mice. The mRNA expression of <span class="html-italic">IL-17A</span> (<b>A</b>,<b>D</b>), <span class="html-italic">RORγt</span> (<b>B</b>,<b>E</b>), and <span class="html-italic">NKp46</span> (<b>C</b>,<b>F</b>) in colon and ileum. Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 7–8, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of IAA on serum concentrations of proinflammatory cytokines in mice. Serum levels of inflammatory cytokine IL-1β (<b>A</b>), TNF-α (<b>B</b>), IL-6 (<b>C</b>) were measured by ELISA. Data were expressed as the mean ± SEM, <span class="html-italic">n</span> = 6, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of IAA supplementation on colonic microbiota of mice. PCoA analysis (<b>A</b>) of β diversity of colonic microbiota on day 4, * <span class="html-italic">p</span> &lt; 0.05. Relative abundance of species at phylum, genus and species levels of colonic microbiota (<b>B</b>), * <span class="html-italic">p</span> &lt; 0.05. Bar of horizontal community composition of phylum (<b>C</b>) and genus (<b>D</b>). Taxonomic cladogram from phylum to species and LDA score plot generated from LEfSe of 16S rRNA gene amplification sequencing data (LDA &gt; 2, <span class="html-italic">p</span> &lt; 0.05) (<b>E</b>,<b>F</b>).</p>
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