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Microorganisms
Volume 12
Issue 6
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Microorganisms, Volume 12, Issue 6 (June 2024) – 220 articles

Cover Story (view full-size image): Children born by cesarean section are described as having a fecal microbiota with altered biodiversity, bifidobacteria and Bacteroides deficiency, and an excess of Enterobacteriaceae. The clinical consequence of this dysbiosis could be an increased incidence of atopy. The focus of this dysbiosis appears to be the reduction in or absence of bifidobacteria species capable of metabolizing milk oligosaccharides, such as B. longum infantis, B. breve, and B. bifidum. The administration of a particular strain of B. bifidum (PRL2010) to children born by cesarean section for the first six months of their lives made it possible to significantly reduce not only the incidence of atopy but also respiratory infections and dyspeptic syndrome. View this paper
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16 pages, 1998 KiB  
Article
Vermicompost Supply Enhances Fragrant-Rice Yield by Improving Soil Fertility and Eukaryotic Microbial Community Composition under Environmental Stress Conditions
by Anas Iqbal, Quaid Hussain, Zhaowen Mo, Tian Hua, Abd El-Zaher M. A. Mustafa and Xiangru Tang
Microorganisms 2024, 12(6), 1252; https://doi.org/10.3390/microorganisms12061252 - 20 Jun 2024
Cited by 5 | Viewed by 1446
Abstract
Heavy-metal contamination in agricultural soil, particularly of cadmium (Cd), poses serious threats to soil biodiversity, rice production, and food safety. Soil microbes improve soil fertility by regulating soil organic matter production, plant nutrient accumulation, and pollutant transformation. Addressing the impact of Cd toxicity [...] Read more.
Heavy-metal contamination in agricultural soil, particularly of cadmium (Cd), poses serious threats to soil biodiversity, rice production, and food safety. Soil microbes improve soil fertility by regulating soil organic matter production, plant nutrient accumulation, and pollutant transformation. Addressing the impact of Cd toxicity on soil fungal community composition, soil health, and rice yield is urgently required for sustainable rice production. Vermicompost (VC) is an organic fertilizer that alleviates the toxic effects of Cd on soil microbial biodiversity and functionality and improves crop productivity sustainably. In the present study, we examined the effects of different doses of VC (i.e., 0, 3, and 6 tons ha−1) and levels of Cd stress (i.e., 0 and 25 mg Cd kg−1) on soil biochemical attributes, soil fungal community composition, and fragrant-rice grain yield. The results showed that the Cd toxicity significantly reduced soil fertility, eukaryotic microbial community composition and rice grain yield. However, the VC addition alleviated the Cd toxicity and significantly improved the soil fungal community; additionally, it enhanced the relative abundance of Ascomycota, Chlorophyta, Ciliophora, Basidiomycota, and Glomeromycta in Cd-contaminated soils. Moreover, the VC addition enhanced the soil’s chemical attributes, including soil pH, soil organic carbon (SOC), available nitrogen (AN), total nitrogen (TN), and microbial biomass C and N, compared to non-VC treated soil under Cd toxicity conditions. Similarly, the VC application significantly increased rice grain yield and decreased the Cd uptake in rice. One possible explanation for the reduced Cd uptake in plants is that VC amendments influence the soil’s biological properties, which ultimately reduces soil Cd bioavailability and subsequently influences the Cd uptake and accumulation in rice plants. RDA analysis determined that the leading fungal species were highly related to soil environmental attributes and microbial biomass C and N production. However, the relative abundance levels of Ascomycota, Basidiomycota, and Glomeromycta were strongly associated with soil environmental variables. Thus, the outcomes of this study reveal that the use of VC in Cd-contaminated soils could be useful for sustainable rice production and safe utilization of Cd-polluted soil. Full article
(This article belongs to the Special Issue Soil Microbial Communities under Environmental Change)
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Figure 1
<p>Venn diagram showing the soil fungal unique and shared operational OTUs in a Cd-contaminated soil under vermicompost application. Note: Please see <a href="#microorganisms-12-01252-t001" class="html-table">Table 1</a> for detailed treatment combinations.</p> Full article ">Figure 2
<p>Influence of vermicompost application in a Cd-contaminated soil on fungal <span class="html-italic">α</span>-diversity. The different boxes for each treatment represent <span class="html-italic">α</span>-diversity estimated by ACE (<b>A</b>), Chao 1 (<b>B</b>), Shannon (<b>C</b>), and Simpson (<b>D</b>) indices. The bars represent the standard error of the mean, with different letters indicating a statistical difference at <span class="html-italic">p</span> &lt; 0.05. Note: Please see <a href="#microorganisms-12-01252-t001" class="html-table">Table 1</a> for detailed treatment combinations.</p> Full article ">Figure 3
<p>Circos diagram showing the relative abundance of the soil microbial communities at the phylum level (<b>A</b>) and class level (<b>B</b>), as affected by vermicompost application in Cd-contaminated soils. Note: Please see <a href="#microorganisms-12-01252-t001" class="html-table">Table 1</a> for detailed treatment combinations.</p> Full article ">Figure 4
<p>Principal component analyses (PCA) of soil fungal communities: (<b>A</b>) soil sampled from each sample, and results from the RDA (<b>B</b>), seeking to explore the relationship among fungal communities and soil biochemical traits. Note: Please see <a href="#microorganisms-12-01252-t001" class="html-table">Table 1</a> for detailed treatment combinations.</p> Full article ">
14 pages, 3010 KiB  
Article
Endophytic Microorganisms in Tomato Roots, Changes in the Structure and Function of the Community at Different Growing Stages
by Yufei Wei, Siyu Chen, Xinyan Zhou, Diancao Ding, Jingjing Song and Shangdong Yang
Microorganisms 2024, 12(6), 1251; https://doi.org/10.3390/microorganisms12061251 - 20 Jun 2024
Cited by 1 | Viewed by 1258
Abstract
This study analyzed flower bud differentiation and fruiting stages to investigate how the structure of the plant endophytic microbial community in the roots of tomatoes changes with plant senescence. Based on high-throughput sequencing technology, the diversity and relative abundance of endophytic microorganisms (bacteria [...] Read more.
This study analyzed flower bud differentiation and fruiting stages to investigate how the structure of the plant endophytic microbial community in the roots of tomatoes changes with plant senescence. Based on high-throughput sequencing technology, the diversity and relative abundance of endophytic microorganisms (bacteria and fungi) in tomato stems at different growth stages were analyzed. At the same time, based on LEfSe analysis, the differences in endophytic microorganisms in tomato stems at different growth stages were studied. Based on PICRUSt2 function prediction and FUNGuild, we predicted the functions of endophytic bacterial and fungal communities in tomato stems at different growth stages to explore potential microbial functional traits. The results demonstrated that not only different unique bacterial genera but also unique fungal genera could be found colonizing tomato roots at different growth stages. In tomato seedlings, flower bud differentiation, and fruiting stages, the functions of colonizing endophytes in tomato roots could primarily contribute to the promotion of plant growth, stress resistance, and improvement in nutrient cycling, respectively. These results also suggest that different functional endophytes colonize tomato roots at different growth stages. Full article
(This article belongs to the Special Issue Using Microorganisms to Increase Crop Productivity and Sustainability)
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Figure 1

Figure 1
<p>Comparison of endophytic microbiota structures in tomato roots at a similarity level of 97% between GY, GH, and GJ treatments ((operational taxonomic unit) OTU level). (<b>a</b>) The Shannon index indicates endophytic bacterial diversity. (<b>b</b>) The Ace index indicates endophytic bacterial richness. (<b>c</b>) The Shannon index indicates endophytic fungal diversity. (<b>d</b>) The Ace index indicates endophytic fungal richness. (<b>e</b>) PCoA score plot of endophytic bacteria communities. (<b>f</b>) PLS-DA score plot of endophytic fungi communities. (<b>g</b>) PCoA score plot of endophytic bacteria communities. (<b>h</b>) PLS-DA score plot of endophytic fungi communities. (<b>i</b>) Venn diagram analyses of endophytic bacteria at the genus level. (<b>j</b>) Venn diagram analyses of endophytic bacteria at the OTU level. (<b>k</b>) Venn diagram analyses of endophytic fungi at the genus level. (<b>l</b>) Venn diagram analyses of endophytic fungi at the OTU level. GY: seedling stage; GH: flower bud differentiation stage; GJ: fruiting period. The same letters on the bars within a figure indicate no significant differences in the mean ranks among treatments at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>(<b>a</b>) Compositions of endophytic bacterial communities at the phylum level; (<b>b</b>) compositions of endophytic fungal communities at the phylum level; (<b>c</b>) compositions of endophytic bacterial communities at the genus level; (<b>d</b>) compositions of endophytic fungal communities at the genus level under the GY, GH, and GJ treatments. GY: seedling stage; GH: flower bud differentiation stage; GJ: fruiting stage.</p> Full article ">Figure 3
<p>LEfSe analysis of endophytic bacteria (<b>a</b>) and fungi (<b>b</b>) in tomato roots under GY, GH, and GJ stages. GY: seedling stage; GH: flower bud differentiation stage; GJ: fruiting stage.</p> Full article ">Figure 4
<p>Relative abundance of BugBase (<b>a</b>), COG (<b>b</b>), and FUNGuild (<b>c</b>) inferred the endophytic microbial functions in tomato roots under GY, flower bud GH, and GJ stages. GY: seedling stage; GH: flower bud differentiation stage; GJ: fruiting period. * <span class="html-italic">p</span> ≤ 0.05.</p> Full article ">
19 pages, 3554 KiB  
Article
Metagenomic Investigation of the Short-Term Temporal and Spatial Dynamics of the Bacterial Microbiome and the Resistome Downstream of a Wastewater Treatment Plant in the Iskar River in Bulgaria
by Deyan Donchev, Ivan N. Ivanov, Ivan Stoikov and Monika Ivanova
Microorganisms 2024, 12(6), 1250; https://doi.org/10.3390/microorganisms12061250 - 20 Jun 2024
Viewed by 1531
Abstract
Waste Water Treatment Plants (WWTP) aim to reduce contamination in effluent water; however, studies indicate antimicrobial resistance genes (ARGs) persist post-treatment, potentially leading to their spread from human populated areas into the environment. This study evaluated the impact of a large WWTP serving [...] Read more.
Waste Water Treatment Plants (WWTP) aim to reduce contamination in effluent water; however, studies indicate antimicrobial resistance genes (ARGs) persist post-treatment, potentially leading to their spread from human populated areas into the environment. This study evaluated the impact of a large WWTP serving 125,000 people on the Iskar River in Bulgaria, by characterizing the spatial and short-term temporal dynamics in bacterial community dynamics and resistance profiles of the surface water. Pairs of samples were collected biweekly on four dates from two different locations, one about 800 m after the WWTP effluents and the other 10 km downstream. Taxonomic classification revealed the dominance of Pseudomonodota and Bacteriodota, notably the genera Flavobacterium, Aquirufa, Acidovorax, Polynucleobacter, and Limnohabitans. The taxonomic structure corresponded with both lentic and lotic freshwater habitats, with Flavobacterium exhibiting a significant decrease over the study period. Principal Coordinate Analysis revealed statistically significant differences in bacterial community composition between samples collected on different dates. Differential abundance analysis identified notable enrichment of Polynucleobacter and Limnohabitans. There were shifts within the enriched or depleted bacterial taxa between early and late sampling dates. High relative abundance of the genes erm(B), erm(F), mph(E), msr(E) (macrolides); tet(C), tet(O), tet(W), tet(Q) and tet(X) (tetracyclines); sul1 and sul2 (sulphonamides); and cfxA3, cfxA6 (beta-lactams) were detected, with trends of increased presence in the latest sampling dates and in the location closer to the WWTP. Of note, genes conferring resistance to carbapenems blaOXA-58 and blaIMP-33-like were identified. Co-occurrence analysis of ARGs and mobile genetic elements on putative plasmids showed few instances, and the estimated human health risk score (0.19) according to MetaCompare2.0 was low. In total, 29 metagenome-assembled genomes were recovered, with only a few harbouring ARGs. This study enhances our understanding of freshwater microbial community dynamics and antibiotic resistance profiles, highlighting the need for continued ARGs monitoring. Full article
(This article belongs to the Special Issue Water Microorganisms Associated with Human Health)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Taxonomic composition bar plot depicts the relative abundance of most abundant genera within each of the eight samples ordered by date and river flow. Low-abundant taxa (&lt;0.1%) and taxa found in less than three samples were filtered out. Counts of level Genus were transformed into relative abundance and taxa below 1.5% were grouped into “Others”. The top 45 genera are designated, coded using distinct colours and ranked from most abundant to least abundant in descending order based on average relative abundance calculated across all samples.</p> Full article ">Figure 2
<p>Alpha diversity and significance testing across samples by collection date and location. Shannon index measures overall diversity, incorporating both species richness (number of species) and evenness (distribution of species), whereas Pielou’s evenness specifically measures evenness. The graphs display on the y-axis the alpha diversity measures (Genus level) of Shannon (<b>A</b>–<b>C</b>) and Pielou’s evenness (<b>D</b>–<b>F</b>). (<b>A</b>,<b>D</b>) each sample individually; (<b>B</b>,<b>E</b>) in groups by collection date; (<b>C</b>,<b>F</b>) in groups by location. The spread and distribution of data points reveal the taxa richness and evenness increased over the time period (<b>B</b>,<b>E</b>) although not significantly (<span class="html-italic">p</span> = 0.08). No significant difference in Shannon and Pielou’s evenness diversity was observed between the two locations (<b>C</b>,<b>F</b>). Low count taxa (<span class="html-italic">n &lt;</span> 5) were filtered out and 20% prevalence filter was applied, meaning that at least 20% of the values of a feature should contain at least five counts. Low variance filter based on standard deviation was also included. Data was normalized using Additive Log Ratio (ALR).</p> Full article ">Figure 3
<p>PERMANOVA tests applied to PCoA data using Bray-Curtis and Jaccard distance metrics to evaluate the differences in bacterial community composition between groups by addressing the abundance and presence/absence of species in each group. The results revealed no statistically significant differences between the groups, as demonstrated by the high F-values (0.68 and 0.72, respectively). The low R<sup>2</sup> values further corroborated this observation, with the groupings accounting for only a small proportion of the overall variability in community composition. These findings indicate that the microbial communities are highly similar, regardless of the distance metric employed.</p> Full article ">Figure 4
<p>Differential abundance analysis (DAA) was utilized to identify enriched (blue) or depleted (orange) taxa when comparing samples both spatially and temporally based on their log-fold change (LFC) values. First, the OTU table was filtered to retain only taxa that were present in all eight samples and accounted for at least 0.1% of the total abundance across all samples. To identify which taxa tend to increase or decrease quantitatively, the ANCOM-BC plugin in Qiime2 was used to compare samples from Mechkata (downstream location) against Dragushinovo (upstream location, regarded as a reference) in each of the four collection dates: (<b>A</b>) 3 November; (<b>B</b>) 17 November; (<b>C</b>) 8 November; (<b>D</b>) 21 December; (<b>E</b>) all samples based on location. For E, less stringent filtering criteria have been applied, where taxa above 0.1% in at least two samples were included. This allowed genera not visible before, such as the genus <span class="html-italic">Aurantimicrobium</span>, to appear. Statistically significant (<span class="html-italic">p</span> &lt; 0.5) results are indicated by red arrows. Different LFC threshold values were applied to all figures for visualization purposes. Full length figures are included in <a href="#app1-microorganisms-12-01250" class="html-app">Figure S3</a>.</p> Full article ">Figure 5
<p>A heatmap graph showing normalized reads mapping to ARGs of clinical importance. ARGs found in fewer than two samples were excluded except for the IMP gene, which is marked with *. Found genes with a minimum of 60% coverage and 60% identity were included. Genes were ordered based on the class of antimicrobials they confer resistance to. Due to the high number of reads mapping to certain genes in comparison to low-represented ones, the colour thresholds were manually adjusted. All genes with <span class="html-italic">n</span> = 50+ reads were coloured the same. MLSB refers to ARGs that affect varying combinations of: erythromycin, azithromycin, telithromycin, lincosamide, streptogramin B/A, lincomycin, clindamycin, quinupristin, and dalfopristine.</p> Full article ">Figure 6
<p>Differential abundance analysis (DAA) was utilized to identify enriched (blue) or depleted (orange) ARG based on their log-fold change (LFC) values between Dragushinovo (closer to the WWTP) and Mechkata locations. Only ARG found in at least four samples were included. The ANCOM-BC plugin in Qiime2 was used to compare samples from Mechkata (downstream location, regarded as a reference) against Dragushinovo (upstream location) in all dates together. Statistically significant (<span class="html-italic">p</span> &lt; 0.5) results are indicated by red arrows.</p> Full article ">Figure 7
<p>Bipartite network graph showing host-ARG associations. Only the 40 most highly represented taxa and additional known human pathogens were included. ARGs found in fewer than two samples were excluded except for the IMP gene, which is marked with *. Green circular nodes represent the genera, whereas orange octagons are ARGs. Edges link nodes from both groups based on positive Pearson correlation (≥0.8) and <span class="html-italic">p</span>-value (≤0.005), and their colour and thickness denote the weight of the connection (purple is higher). Positive correlations indicate a high theoretical probability of a taxon acting as a host for an ARG. Node sizes of genera denote the centrality of each node to the network. The visual length of the edges should be ignored, as the graph layout was manually adjusted to enhance readability. Please note that not all edges in the network may represent meaningful relationships.</p> Full article ">
23 pages, 2670 KiB  
Article
Evaluation of Functional Properties of Some Lactic Acid Bacteria Strains for Probiotic Applications in Apiculture
by Adriana Cristina Urcan, Adriana Dalila Criste, Otilia Bobiș, Mihaiela Cornea-Cipcigan, Alexandru-Ioan Giurgiu and Daniel Severus Dezmirean
Microorganisms 2024, 12(6), 1249; https://doi.org/10.3390/microorganisms12061249 - 20 Jun 2024
Cited by 3 | Viewed by 1881
Abstract
This study evaluates the suitability of three lactic acid bacteria (LAB) strains—Lactiplantibacillus plantarum, Lactobacillus acidophilus, and Apilactobacillus kunkeei—for use as probiotics in apiculture. Given the decline in bee populations due to pathogens and environmental stressors, sustainable alternatives to conventional [...] Read more.
This study evaluates the suitability of three lactic acid bacteria (LAB) strains—Lactiplantibacillus plantarum, Lactobacillus acidophilus, and Apilactobacillus kunkeei—for use as probiotics in apiculture. Given the decline in bee populations due to pathogens and environmental stressors, sustainable alternatives to conventional treatments are necessary. This study aimed to assess the potential of these LAB strains in a probiotic formulation for bees through various in vitro tests, including co-culture interactions, biofilm formation, auto-aggregation, antioxidant activity, antimicrobial activity, antibiotic susceptibility, and resistance to high osmotic concentrations. This study aimed to assess both the individual effects of the strains and their combined effects, referred to as the LAB mix. Results indicated no mutual antagonistic activity among the LAB strains, demonstrating their compatibility with multi-strain probiotic formulations. The LAB strains showed significant survival rates under high osmotic stress and simulated gastrointestinal conditions. The LAB mix displayed enhanced biofilm formation, antioxidant activity, and antimicrobial efficacy against different bacterial strains. These findings suggest that a probiotic formulation containing these LAB strains could be used for a probiotic formulation, offering a promising approach to mitigating the negative effects of pathogens. Future research should focus on in vivo studies to validate the efficacy of these probiotic bacteria in improving bee health. Full article
(This article belongs to the Section Veterinary Microbiology)
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Figure 1

Figure 1
<p>Growth of <span class="html-italic">L. plantarum</span>, <span class="html-italic">A. kunkeei</span>, and <span class="html-italic">L. acidophilus</span> is independent and in co-culture (LAB mix). Values are represented as the means of three independent determinations ± standard deviation. Lowercase letters indicate significant differences among the evaluated LABs at different time intervals (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>The effect of different syrup concentrations on LAB mix development. (<b>a</b>) Development of LAB mix in different concentrations of sugar syrup; (<b>b</b>) development of LAB mix in different concentrations of glucose + fructose syrup. For both graphs, the positive control is represented by the MRS medium with LAB-mixed bacteria, and the negative control is represented by the MRS medium without bacteria. Values are represented as the mean of three independent determinations ± standard deviation, highlighting the variability in the LAB response. Asterisks indicate significant differences between the development rate of LABs in different concentrations of syrups (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 3
<p>The Kaplan–Meier survival curve of LAB mix in syrup over a period of 7 days. Sugar syrup = survival rate of LAB in sugar syrup (blue line). Glucose + fructose syrup = survival rate of LAB in glucose + fructose syrup. Control = LAB without syrup, with MRS medium. Curves labeled with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Survival rate of (<b>a</b>) <span class="html-italic">L. plantarum</span>, (<b>b</b>) <span class="html-italic">L. acidophilus</span>, (<b>c</b>) <span class="html-italic">A. kunkeei</span>, and (<b>d</b>) LAB mix in simulated in vitro gastric (t &lt; 2 h) and intestinal (t &gt; 2 h) conditions. The blue line (spherical markers) indicates LAB mixes with sugar syrup, and the red line (square markers) indicates LAB mixes with glucose and fructose syrup incubated with gastric and intestinal juices. Values are represented as the mean of three independent determinations ± standard deviation. Asterisks indicate significant differences between the survival rate of LABs in gastric and intestinal conditions based on the syrup mixtures (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 5
<p>Comparison of capacities for biofilm formation by tested LAB. Influence of MRS medium supplementation with sucrose, glucose, and fructose syrups on biofilm formation. Values represent the mean of six biological repeats ± standard deviation. Different letters indicate statistically significant differences between groups (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Growth inhibition (%) of pathogenic strains: (<b>a</b>) <span class="html-italic">S. aureus</span>, (<b>b</b>) <span class="html-italic">B. cereus</span>, (<b>c</b>) <span class="html-italic">S. enteritidis</span>, (<b>d</b>) <span class="html-italic">E. coli</span>, (<b>e</b>) <span class="html-italic">P. aeruginosa</span>, (<b>f</b>) <span class="html-italic">E. faecalis</span>, (<b>g</b>) <span class="html-italic">M. plutonius</span>, (<b>h</b>) <span class="html-italic">P. larvae</span>, (<b>i</b>) <span class="html-italic">P. alvei</span>, (<b>j</b>) <span class="html-italic">C. albicans</span> by LAB supernatants at physiological pH of LAB evaluated by microplate method. The dark blue on the graphic represents the concentration of 50% of LAB supernatants, the blue color represents the concentration of 25% of LAB supernatants, and the light blue color represents the concentration of 12.5% of LAB supernatants. Values are represented as the mean of three independent determinations ± standard deviation. Different letters between the same LAB supernatant concentration denote significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
17 pages, 1990 KiB  
Review
The Function of Probiotics and Prebiotics on Canine Intestinal Health and Their Evaluation Criteria
by Junliang Xia, Yuling Cui, Yan Guo, Yuwen Liu, Baichuan Deng and Sufang Han
Microorganisms 2024, 12(6), 1248; https://doi.org/10.3390/microorganisms12061248 - 20 Jun 2024
Cited by 2 | Viewed by 3088
Abstract
Maintaining homeostasis within the intestinal microbiota is imperative for assessing the health status of hosts, and dysbiosis within the intestinal microbiota is closely associated with canine intestinal diseases. In recent decades, the modulation of canine intestinal health through probiotics and prebiotics has emerged [...] Read more.
Maintaining homeostasis within the intestinal microbiota is imperative for assessing the health status of hosts, and dysbiosis within the intestinal microbiota is closely associated with canine intestinal diseases. In recent decades, the modulation of canine intestinal health through probiotics and prebiotics has emerged as a prominent area of investigation. Evidence indicates that probiotics and prebiotics play pivotal roles in regulating intestinal health by modulating the intestinal microbiota, fortifying the epithelial barrier, and enhancing intestinal immunity. This review consolidates literature on using probiotics and prebiotics for regulating microbiota homeostasis in canines, thereby furnishing references for prospective studies and formulating evaluation criteria. Full article
(This article belongs to the Special Issue Shaping the Future of Probiotics: Novel Methodologies, Applications, and Mechanisms of Action, 2nd Edition)
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<p>The interaction between intestinal microbiota and probiotics and prebiotics (by figdraw). The interaction between probiotics and intestinal microbiota tends to be antagonistic. Probiotics will compete with the intestinal flora for nutrients and inhibit the colonization of the intestinal microbiota through secreting antimicrobial peptides and bacteriocins. On the other hand, probiotics or intestinal microbiota will metabolize the prebiotics to produce organic acids (e.g., short-chain fatty acids and lactic acid), which lower the pH in the intestinal tract. Eventually, the number of beneficial bacteria increases, the number of pathogenic bacteria decreases, and the intestinal microecology reaches balance with the action of low pH value and substances such as antimicrobial peptides and bacteriocins.</p> Full article ">Figure 2
<p>Activation of intestinal immunology via probiotics and prebiotics (by figdraw). The MAMPs of probiotics can be recognized via PPRs expressed on the epithelial cell membrane, thereby activating the transcription of cytokines through signaling pathways such as NF-κB. Cytokines produced by intestinal epithelial cells can trigger the native immune response in the mucous layer. For instance, this molecule can transactivate monocytes and dendritic cells to secrete chemokines and other cytokines (e.g., TNF) that further promote the proliferation and differentiation of T lymphocytes. Moreover, B cells rapidly respond to these signals by differentiating into plasma cells and secreting antibodies (e.g., IgA) into the intestinal lumen. The IgA forms a line of defense on epithelial cells and binds to receptors on the surface of pathogenic bacteria, promoting bacterial lysis and clearance.</p> Full article ">Figure 3
<p>Modulation of intestinal mucous immune function by SCFAs produced by probiotics and prebiotics (by figdraw). Probiotics and intestinal microorganisms metabolize dietary fibers, such as prebiotics, to produce SCFAs, which pass through the intestinal epithelial layer into the mucous layer and modulate the function of innate immune cells. For instance, SCFAs can activate monocytes and dendritic cells to enhance their phagocytosis and secretion, promote the differentiation of T-lymphocytes to regulatory T cells and helper T cells, and promote the proliferation and differentiation of B-lymphocytes. In addition, SCFAs can bind to GPCRs and mediate the transcription and translation of cytokines (e.g., IL-12) through activating pathways such as JAK/STAT, NF-κB, and MAPK. On the other hand, SCFAs also act as inhibitors of HDACs to enhance the expression of immune factors.</p> Full article ">
14 pages, 2625 KiB  
Article
Whole-Genome Sequencing of Two Potentially Allelopathic Strains of Bacillus from the Roots of C. equisetifolia and Identification of Genes Related to Synthesis of Secondary Metabolites
by Ying Wang, Pan Chen, Qi Lin, Linzhi Zuo and Lei Li
Microorganisms 2024, 12(6), 1247; https://doi.org/10.3390/microorganisms12061247 - 20 Jun 2024
Viewed by 1174
Abstract
The coastal Casuarina equisetifolia is the most common tree species in Hainan’s coastal protection forests. Sequencing the genomes of its allelopathic endophytes can allow the protective effects of these bacteria to be effectively implemented in protected forests. The goal of this study was [...] Read more.
The coastal Casuarina equisetifolia is the most common tree species in Hainan’s coastal protection forests. Sequencing the genomes of its allelopathic endophytes can allow the protective effects of these bacteria to be effectively implemented in protected forests. The goal of this study was to sequence the whole genomes of the endophytes Bacillus amyloliquefaciens and Bacillus aryabhattai isolated from C. equisetifolia root tissues. The results showed that the genome sizes of B. amyloliquefaciens and B. aryabhattai were 3.854 Mb and 5.508 Mb, respectively. The two strains shared 2514 common gene families while having 1055 and 2406 distinct gene families, respectively. The two strains had 283 and 298 allelochemical synthesis-associated genes, respectively, 255 of which were shared by both strains and 28 and 43 of which were unique to each strain, respectively. The genes were putatively involved in 11 functional pathways, including secondary metabolite biosynthesis, terpene carbon skeleton biosynthesis, biosynthesis of ubiquinone and other terpene quinones, tropane/piperidine and piperidine alkaloids biosynthesis, and phenylpropanoid biosynthesis. NQO1 and entC are known to be involved in the biosynthesis of ubiquinone and other terpenoid quinones, and rfbC/rmlC, rfbA/rmlA/rffH, and rfbB/rmlB/rffG are involved in the biosynthesis of polyketide glycan units. Among the B. aryabhattai-specific allelochemical synthesis-related genes, STE24 is involved in terpene carbon skeleton production, atzF and gdhA in arginine biosynthesis, and TYR in isoquinoline alkaloid biosynthesis. B. amyloliquefaciens and B. aryabhattai share the genes aspB, yhdR, trpA, trpB, and GGPS, which are known to be involved in the synthesis of carotenoids, indole, momilactones, and other allelochemicals. Additionally, these bacteria are involved in allelochemical synthesis via routes such as polyketide sugar unit biosynthesis and isoquinoline alkaloid biosynthesis. This study sheds light on the genetic basis of allelopathy in Bacillus strains associated with C. equisetifolia, highlighting the possible use of these bacteria in sustainable agricultural strategies for weed management and crop protection. Full article
(This article belongs to the Section Plant Microbe Interactions)
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Figure 1
<p>Genomic maps of XYG6 and XAG3. (<b>A</b>) Genomic map of XYG6. (<b>B</b>) Genomic map of XAG3.</p> Full article ">Figure 2
<p>Functional annotation of the genes in the XYG6 and XAG3 strains. (<b>A</b>) Phylogenetic tree based on the 16S rDNA gene sequences. (<b>B</b>) Gene families are common and specific to XYG6 and XAG3. KEGG functional classification annotations for the XYG6 (<b>C</b>) and XAG3 (<b>D</b>) strains.</p> Full article ">Figure 2 Cont.
<p>Functional annotation of the genes in the XYG6 and XAG3 strains. (<b>A</b>) Phylogenetic tree based on the 16S rDNA gene sequences. (<b>B</b>) Gene families are common and specific to XYG6 and XAG3. KEGG functional classification annotations for the XYG6 (<b>C</b>) and XAG3 (<b>D</b>) strains.</p> Full article ">Figure 2 Cont.
<p>Functional annotation of the genes in the XYG6 and XAG3 strains. (<b>A</b>) Phylogenetic tree based on the 16S rDNA gene sequences. (<b>B</b>) Gene families are common and specific to XYG6 and XAG3. KEGG functional classification annotations for the XYG6 (<b>C</b>) and XAG3 (<b>D</b>) strains.</p> Full article ">Figure 3
<p>Allelochemical synthesis-related genes in the XYG6 and XAG3 genomes. (<b>A</b>) The number of allelochemical synthesis-related genes common and unique to the XYG6 and XAG3 genomes. (<b>B</b>) The number of allelochemical synthesis-related genes in the XYG6 and XAG3 genomes and their corresponding functional pathways.</p> Full article ">
17 pages, 4561 KiB  
Article
Bacillus amyloliquefaciens LM-1 Affects Multiple Cell Biological Processes in Magnaporthe oryzae to Suppress Rice Blast
by Meiling Liang, Aiqing Feng, Congying Wang, Xiaoyuan Zhu, Jing Su, Zihan Xu, Jianyuan Yang, Wenjuan Wang, Kailing Chen, Bing Chen, Xiaopeng Lin, Jinqi Feng and Shen Chen
Microorganisms 2024, 12(6), 1246; https://doi.org/10.3390/microorganisms12061246 - 20 Jun 2024
Viewed by 1400
Abstract
Magnaporthe oryzae, one of the most destructive rice pathogens, causes significant losses during the rice harvest every year. Bacillus amyloliquefaciens has been explored in many crops as a potential biocontrol agent. However, the mechanisms of B. amyloliquefaciens controled rice blast are not [...] Read more.
Magnaporthe oryzae, one of the most destructive rice pathogens, causes significant losses during the rice harvest every year. Bacillus amyloliquefaciens has been explored in many crops as a potential biocontrol agent. However, the mechanisms of B. amyloliquefaciens controled rice blast are not fully understood. Here, a biocontrol strain LM-1, isolated from a contaminated medium, was identified as B. amyloliquefaciens using morphological observation, physiological and biochemical tests, and 16S rDNA sequencing. LM-1 inhibited the growth and pathogenicity of M. oryzae and Bipolaris oryzae (Breda de Haan) Shoem. The mycelia of M. oryzae co-cultured with LM-1 were enlarged and broken by fluorescence microscopy using calcofluor white. LM-1 inhibited the mycelia of M. oryzae from producing conidia. Genes itu, srf, and fenB were detected in LM-1. Furthermore, the supernatant of LM-1 interfered with the appressorium formation of M. oryzae, blocked conidial cell death, and reduced autophagy degradation but did not affect the normal germination of rice seeds and seeding growth. Additionally, we observed hypersensitivity reactions, reactive oxygen species, and iron accumulation reduction in rice cells inoculated with supernatant. Our study reveals that LM-1 has a control effect on rice blast and affects cell wall integrity, sporulation, appressorium formation, cell death, and autophagy. Full article
(This article belongs to the Section Microbial Biotechnology)
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<p>Isolation and identification of <span class="html-italic">B. amyloliquefaciens</span> LM-1. (<b>A</b>) <span class="html-italic">M. oryzae</span> + LM-1 were co-cultured on PA medium. (<b>B</b>) LM-1 was cultured on LB medium. (<b>C</b>) LM-1 was observed by transmission electron microscope (TEM). Scale bar = 2.0 µm. (<b>D</b>) LM-1 was dyed by gram staining. Scale bar = 20 µm.</p> Full article ">Figure 2
<p>The phylogenetic tree of strain LM-1 with its close relatives <span class="html-italic">Bacillus</span> species based on 16S rDNA gene sequencing. Neighbor-Joining phylogenetic tree of the strain LM-1 was constructed by MEGA 7.0. The phylogenetic tree of <span class="html-italic">B. amyloliquefaciens</span> LM-1 and 12 other <span class="html-italic">Bacillus</span> species were based on 16S rDNA sequence analysis. The numbers at the branches indicate the confidence level calculated by bootstrap analysis (1000).</p> Full article ">Figure 3
<p>Genomic Features of <span class="html-italic">B. amyloliquefaciens</span> LM-1. Circular genome map of <span class="html-italic">B. amyloliquefaciens</span> LM-1 was shown from the outside to the inside, GC content, sequencing depth, gene elements and COG functions.</p> Full article ">Figure 4
<p>Enzyme activity detection of <span class="html-italic">B. amyloliquefaciens</span> LM-1. (<b>A</b>,<b>C</b>): amylase (Aml) and protease (Prt) production detection of bioassay plate, the statistics of Aml and Prt degradation diameter. LB liquid medium served as a negative control (CK). (<b>B</b>,<b>D</b>) cellulase (Cel) and pectate lyase (Pel) production detection of bioassay plate, the statistics of Cel and Pel degradation diameter. <span class="html-italic">Dickeya zeae</span> EC1 was used as a positive control (CK).</p> Full article ">Figure 5
<p>The Antifungal activity test between LM-1 and fungal pathogens. Fungal plate photographs were taken after 7 days. All plates were placed at 28 °C. <span class="html-italic">M. o</span>: <span class="html-italic">M. oryzae</span>, <span class="html-italic">B. o</span>: <span class="html-italic">B. oryzae</span>.</p> Full article ">Figure 6
<p>The biocontrol efficacy of strain LM-1 on rice blast. Pathogenesis assay with detached rice leaves (CO39). FTB, BTS, and SUP. were added to conidial suspension concurrently (0 h), 24 h before (−24 h), or after (+24 h) conidia inoculation on detached rice leaves. Detached rice leaves were incubated with the FTB, BTC, and SUP, respectively, with water as a negative control (CK). Images were taken at 7 days.</p> Full article ">Figure 7
<p>LM-1 inhibits the infection in the rice leaf sheath. (<b>A</b>) Effect of strain LM-1 on rice sheath inoculation during <span class="html-italic">M. oryzae</span> infection. <span class="html-italic">M. o</span>: suspension concentration 10<sup>5</sup> conidia/mL in water; <span class="html-italic">M. o</span> + LM-1: suspension concentration 10<sup>5</sup> conidia/mL with the SUP of LM-1; BF: bright field; GFP: green fluorescent protein. Ap: appressorium. My: mycelium. Scale bar = 10 µm. (<b>B</b>) Effect of LM-1 on rice HR, ROS, and Fe<sup>3+</sup> accumulation during <span class="html-italic">M. oryzae</span> infection. HR, DAB, and Prussian blue staining were performed on the conidia-inoculated rice leaf sheath at 48 h. <span class="html-italic">M. o</span>: suspension concentration 10<sup>5</sup> conidia/mL in water; <span class="html-italic">M. o</span> + LM-1: suspension concentration 10<sup>5</sup> conidia/mL with the SUP of LM-1. Ap: appressorium. My: mycelium. CO: conidium. Scale bar = 10 µm.</p> Full article ">Figure 8
<p>LM-1 inhibits sporulation and damages the integrity of the cell wall in <span class="html-italic">M. oryzae.</span> (<b>A</b>) LM-1 inhibited sporulation of <span class="html-italic">M. oryzae</span>. <span class="html-italic">M. o</span>: suspension concentration 10<sup>5</sup> conidia/mL in water; <span class="html-italic">M. o</span> + LM-1: suspension concentration 10<sup>5</sup> conidia/mL with the SUP of LM-1. The black arrows indicated conidia. Scale bar = 20 μm. (<b>B</b>) LM-1 destroyed cell wall integrity of <span class="html-italic">M. oryzae</span>. LM-1 altered the distribution of chitin in the cell wall of <span class="html-italic">M. oryzae</span>. Hyphae were stained with 10 μg/mL calcofluor white (CFW) for 5 min in dark before being photographed. BF: bright field. The red arrow indicated mycelium enlargement. Scale bar = 10 μm.</p> Full article ">Figure 9
<p><span class="html-italic">B. amyloliquefaciens</span> LM-1 suppresses appressorium formation and conidial cell death of <span class="html-italic">M. oryzae.</span> Live cell imaging experiment to show strain hH1-GFP localization during <span class="html-italic">M. oryzae</span> appressorium development (2, 8, and 24 h) with or without the SUP of LM-1. <span class="html-italic">M. o</span>: suspension concentration 10<sup>5</sup> conidia/mL in water; <span class="html-italic">M. o</span> + LM-1: suspension concentration 10<sup>5</sup> conidia/mL with the SUP of LM-1. BF: bright field. GFP: green fluorescent protein. Scale bar = 10 μm.</p> Full article ">Figure 10
<p>Effects of <span class="html-italic">B. amyloliquefaciens</span> LM-1 on <span class="html-italic">M. oryzae</span> autophagy. Epifluorescence images showing subcellular localization of GFP-Atg8 with or without the SUP of LM-1 during conidial development (2 and 6 h) are representatives of three biological replicates of the experiment. BF: bright field, GFP: green fluorescent protein. Scale bar = 10 μm.</p> Full article ">Figure 11
<p>The germination of rice seeds and plant growth were not affected by <span class="html-italic">B. amyloliquefaciens</span> LM-1. (<b>A</b>) Rice seed germination. (<b>B</b>) The growth of rice root and sprout, Water: water treatment, LM-1: <span class="html-italic">B. amyloliquefaciens</span> LM-1. (<b>C</b>,<b>D</b>) the data for the rice germination rate%, and the length of rice root and sprout.</p> Full article ">
15 pages, 4331 KiB  
Article
Functional Genes and Transcripts Indicate the Existent and Active Microbial Mercury-Methylating Community in Mangrove Intertidal Sediments of an Urbanized Bay
by Guofang Feng and Sanqiang Gong
Microorganisms 2024, 12(6), 1245; https://doi.org/10.3390/microorganisms12061245 - 20 Jun 2024
Viewed by 1059
Abstract
Mercury (Hg) methylation in mangrove sediments can result in the accumulation of neurotoxic methylmercury (MeHg). Identification of Hg methyltransferase gene hgcA provides the means to directly characterize the microbial Hg-methylating consortia in environments. Hitherto, the microbial Hg-methylating community in mangrove sediments was scarcely [...] Read more.
Mercury (Hg) methylation in mangrove sediments can result in the accumulation of neurotoxic methylmercury (MeHg). Identification of Hg methyltransferase gene hgcA provides the means to directly characterize the microbial Hg-methylating consortia in environments. Hitherto, the microbial Hg-methylating community in mangrove sediments was scarcely investigated. An effort to assess the diversity and abundance of hgcA genes and transcripts and link them to Hg and MeHg contents was made in the mangrove intertidal sediments along the urbanized Shenzhen Bay, China. The hgcA genes and transcripts associated with Thermodesulfobacteria [mainly Geobacteraceae, Syntrophorhabdaceae, Desulfobacterales, and Desulfarculales (these four lineages were previously classified into the Deltaproteobacteria taxon)], as well as Euryarchaeota (mainly Methanomicrobia and Theionarchaea) dominated the hgcA-harboring communities, while Chloroflexota, Nitrospirota, Planctomycetota, and Lentisphaerota-like hgcA sequences accounted for a small proportion. The hgcA genes appeared in greater abundance and diversity than their transcript counterparts in each sampling site. Correlation analysis demonstrated that the MeHg content rather than Hg content significantly correlated with the structure of the existent/active hgcA-harboring community and the abundance of hgcA genes/transcripts. These findings provide better insights into the microbial Hg methylation drivers in mangrove sediments, which could be helpful for understanding the MeHg biotransformation therein. Full article
(This article belongs to the Special Issue Genomics of Marine and Aquatic Bacteria: A Focus on Novel Taxa, Diversity and Biotechnological Potential: 2nd Edition)
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<p>Sampling sites in mangrove sediments of Shenzhen Bay.</p> Full article ">Figure 2
<p>THg and MeHg contents in the sampling sites M1, M2, and M3 of mangrove sediments (<b>A</b>); rarefaction curve analysis of the <span class="html-italic">hgcA</span> sequences from the gene–based libraries M1D, M2D, and M3D, and transcript–based libraries M1R, M2R, and M3R, based on 80% sequence identity (<b>B</b>); Venn analysis of the <span class="html-italic">hgcA</span> phylotypes derived from libraries (<b>C</b>).</p> Full article ">Figure 3
<p>Principal coordinates analysis (PCoA) plot of thetaYC matrix distances for <span class="html-italic">hgcA</span> gene (M1D, M2D, and M3D) and transcript (M1R, M2R, and M3R)–harboring communities derived from different sampling sites of mangrove sediments (<b>A</b>). Hierarchical clustering heatmap derived from Bray–Curtis matrix distances of <span class="html-italic">hgcA</span> genes and transcript–harboring communities (<b>B</b>).</p> Full article ">Figure 4
<p>Phylogenetic analysis of the <span class="html-italic">hgcA</span> phylotype representatives and their most similar environmental sequences (bold font) identified by BLASTn search in NCBI at the amino acid level. The scale bar represents a 10% sequence divergence per homologous position. The arrow represents the <span class="html-italic">hgcA</span> sequence (JCM21531_3779) of a Firmicutes Hg–methylating bacteria, <span class="html-italic">Hungateiclostridium straminisolvens</span> JCM 21531. Representatives of the <span class="html-italic">hgcA</span> phylotypes were deposited in GenBank under the accession numbers MN475572~MN475689.</p> Full article ">Figure 5
<p>CCA between THg or MeHg content and the structure of <span class="html-italic">hgcA</span> genes or transcript library originated from the sampling sites M1, M2, and M3; the triangles represent <span class="html-italic">hgcA</span> phylotypes (<b>A</b>); quantitative abundance of <span class="html-italic">hgcA</span> genes and <span class="html-italic">hgcA</span> transcripts and the <span class="html-italic">hgcA</span> transcript–to–gene ratio from the sampling sites M1, M2, and M3 of mangrove sediments; the error bar represents the standard deviation (<b>B</b>).</p> Full article ">
12 pages, 6006 KiB  
Article
HigA2 (Rv2021c) Is a Transcriptional Regulator with Multiple Regulatory Targets in Mycobacterium tuberculosis
by Mingyan Xu, Meikun Liu, Tong Liu, Xuemei Pan, Qi Ren, Tiesheng Han and Lixia Gou
Microorganisms 2024, 12(6), 1244; https://doi.org/10.3390/microorganisms12061244 - 20 Jun 2024
Viewed by 1420
Abstract
Toxin-antitoxin (TA) systems are the major mechanism for persister formation in Mycobacterium tuberculosis (Mtb). Previous studies found that HigBA2 (Rv2022c-Rv2021c), a predicted type II TA system of Mtb, could be activated for transcription in response to multiple stresses such as [...] Read more.
Toxin-antitoxin (TA) systems are the major mechanism for persister formation in Mycobacterium tuberculosis (Mtb). Previous studies found that HigBA2 (Rv2022c-Rv2021c), a predicted type II TA system of Mtb, could be activated for transcription in response to multiple stresses such as anti-tuberculosis drugs, nutrient starvation, endure hypoxia, acidic pH, etc. In this study, we determined the binding site of HigA2 (Rv2021c), which is located in the coding region of the upstream gene higB2 (Rv2022c), and the conserved recognition motif of HigA2 was characterized via oligonucleotide mutation. Eight binding sites of HigA2 were further found in the Mtb genome according to the conserved motif. RT-PCR showed that HigA2 can regulate the transcription level of all eight of these genes and three adjacent downstream genes. DNA pull-down experiments showed that twelve functional regulators sense external regulatory signals and may regulate the transcription of the HigBA2 system. Of these, Rv0903c, Rv0744c, Rv0474, Rv3124, Rv2603c, and Rv3583c may be involved in the regulation of external stress signals. In general, we identified the downstream target genes and possible upstream regulatory genes of HigA2, which paved the way for the illustration of the persistence establishment mechanism in Mtb. Full article
(This article belongs to the Special Issue Transcriptional Regulation in Bacteria)
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<p><span class="html-italic">M. smegmatis</span> growth performance check. Bacterial solution was adjusted to OD<sub>600</sub> = 0.2. The resuspension was diluted 10<sup>−1</sup>, 10<sup>−2</sup>, 10<sup>−3</sup>, and 10<sup>−4</sup>-fold, and each diluted sample was spotted onto 7H10 solid medium with or without 0.2% Acetamide.</p> Full article ">Figure 2
<p>EMSA assay of HigA2 protein with possible binding sites. (<b>A</b>) C-terminal 6×His-tagged HigA2 was successfully expressed and purified. (<b>B</b>) PCR assay of <span class="html-italic">Rv2023c-higB2</span> and <span class="html-italic">higB2-higA2</span> intergenic regions in cDNA and gDNA of <span class="html-italic">Mtb</span> H37Rv. (<b>C</b>) The location of the HigA2 EMSA oligos. (<b>D</b>) EMSA detection of probe seq1-seq7 with HigA2.</p> Full article ">Figure 3
<p>EMSA assay of HigA2 protein with promoter DNA mutants. (<b>A</b>) Sequences of seq6 and the eight mutants, with substitutions indicated by lowercase letters. Flag region is the inverted repeat sequence outlined and underlined, and the inter-region is the sequence between the two repeats. (<b>B</b>) EMSA experiments were performed on HigA2 and 8 mutant oligos.</p> Full article ">Figure 4
<p>EMSA assay of HigA2 on the target oligos. (<b>A</b>–<b>H</b>) Eight oligos labeled with a length of 40 bp were all at a concentration of 1 μM and co-incubated with increasing concentrations of HigA2 protein.</p> Full article ">Figure 5
<p>The expression of HigA2 downstream gene. The expression of HigA2 downstream genes was analyzed by reverse transcription PCR in the Δ<span class="html-italic">higA</span> mutant strain. The data are expressed as the relative fold expression of mRNA compared to <span class="html-italic">sigA</span>, the endogenous control.</p> Full article ">Figure 6
<p>Illustration of DNA pull-down probes. Illustration of DNA pull-down-1 and DNA pull-down-2 in <span class="html-italic">Rv2023c-higB2-higB2</span>.</p> Full article ">
10 pages, 2182 KiB  
Article
Parasitic Effects on the Congenital Transmission of Trypanosoma cruzi in Mother–Newborn Pairs
by Ana Gabriela Herrera Choque, Washington R. Cuna, Simona Gabrielli, Simonetta Mattiucci, Roberto Passera and Celeste Rodriguez
Microorganisms 2024, 12(6), 1243; https://doi.org/10.3390/microorganisms12061243 - 20 Jun 2024
Viewed by 1096
Abstract
Maternal parasitemia and placental parasite load were examined in mother–newborn pairs to determine their effect on the congenital transmission of Trypanosoma cruzi. Parasitemia was qualitatively assessed in mothers and newborns by the microhematocrit test; parasite load was determined in the placental tissues [...] Read more.
Maternal parasitemia and placental parasite load were examined in mother–newborn pairs to determine their effect on the congenital transmission of Trypanosoma cruzi. Parasitemia was qualitatively assessed in mothers and newborns by the microhematocrit test; parasite load was determined in the placental tissues of transmitting and non-transmitting mothers by the detection of T. cruzi DNA and by histology. Compared to transmitter mothers, the frequency and prevalence of parasitemia were found to be increased in non-transmitter mothers; however, the frequency and prevalence of parasite load were higher among the transmitter mothers than among their non-transmitter counterparts. Additionally, serum levels of interferon (IFN)-γ were measured by an enzyme-linked immunosorbent assay (ELISA) in peripheral, placental, and cord blood samples. Median values of IFN-γ were significantly increased in the cord blood of uninfected newborns. The median IFN-γ values of transmitter and non-transmitter mothers were not significantly different; however, non-transmitter mothers had the highest total IFN-γ production among the group of mothers. Collectively, the results of this study suggest that the anti-T. cruzi immune response occurring in the placenta and cord is under the influence of the cytokines from the mother’s blood and results in the control of parasitemia in uninfected newborns. Full article
(This article belongs to the Special Issue Parasites and Infection: Strategies to Control, Diagnose, and Treat Parasitic Diseases)
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<p>Results of N-PCR amplifications with <span class="html-italic">T. cruzi</span> DNA nuclear, electrophoresed on a 2% agarose gel and visualized by ethidium bromide staining. The 149 base pairs (bp) were amplified through N-PCR with primers TCZ3 and TCZ4. M: molecular weight marker (50 bp); C+: positive control (<span class="html-italic">T. cruzi</span> II of Y strain); S1–S7: representative amplicons of positive patients, from transmitter (S2–S4) and non-transmitter (S1, S5–S7) mothers; C−: negative control from a patient with negative serology for <span class="html-italic">T. cruzi</span>.</p> Full article ">Figure 2
<p>Histological section of the placenta from a non-transmitter mother. Arrows point to (<b>A</b>,<b>B</b>) amastigote nests; and (<b>C</b>) released parasites (H&amp;E). Scale bar: 25 μm.</p> Full article ">Figure 3
<p>Histological section of the placenta from a transmitter mother. Arrows point to (<b>A</b>) amastigote nest; and (<b>B</b>,<b>C</b>) released parasites (H&amp;E). Scale bar: 25 μm.</p> Full article ">
15 pages, 1413 KiB  
Article
Searching for Chemical Agents Suppressing Substrate Microbiota in White-Rot Fungi Large-Scale Cultivation
by Audrius Maruška, Rūta Mickienė, Vilma Kaškonienė, Saulius Grigiškis, Mantas Stankevičius, Tomas Drevinskas, Olga Kornyšova, Enrica Donati, Nicola Tiso, Jurgita Mikašauskaitė-Tiso, Massimo Zacchini, Donatas Levišauskas, Ona Ragažinskienė, Kristina Bimbiraitė-Survilienė, Arvydas Kanopka and Gediminas Dūda
Microorganisms 2024, 12(6), 1242; https://doi.org/10.3390/microorganisms12061242 - 20 Jun 2024
Cited by 1 | Viewed by 1305
Abstract
Edible fungi are a valuable resource in the search for sustainable solutions to environmental pollution. Their ability to degrade organic pollutants, extract heavy metals, and restore ecological balance has a huge potential for bioremediation. They are also sustainable food resources. Edible fungi (basidiomycetes [...] Read more.
Edible fungi are a valuable resource in the search for sustainable solutions to environmental pollution. Their ability to degrade organic pollutants, extract heavy metals, and restore ecological balance has a huge potential for bioremediation. They are also sustainable food resources. Edible fungi (basidiomycetes or fungi from other divisions) represent an underutilized resource in the field of bioremediation. By maximizing their unique capabilities, it is possible to develop innovative approaches for addressing environmental contamination. The aim of the present study was to find selective chemical agents suppressing the growth of microfungi and bacteria, but not suppressing white-rot fungi, in order to perform large-scale cultivation of white-rot fungi in natural unsterile substrates and use it for different purposes. One application could be the preparation of a matrix composed of wooden sleeper (contaminated with PAHs) and soil for further hazardous waste bioremediation using white-rot fungi. In vitro microbiological methods were applied, such as, firstly, compatibility tests between bacteria and white-rot fungi or microfungi, allowing us to evaluate the interaction between different organisms, and secondly, the addition of chemicals on the surface of a Petri dish with a test strain of microorganisms of white-rot fungi, allowing us to determine the impact of chemicals on the growth of organisms. This study shows that white-rot fungi are not compatible to grow with several rhizobacteria or bacteria isolated from soil and bioremediated waste. Therefore, the impact of several inorganic materials, such as lime (hydrated form), charcoal, dolomite powder, ash, gypsum, phosphogypsum, hydrogen peroxide, potassium permanganate, and sodium hydroxide, was evaluated on the growth of microfungi (sixteen strains), white-rot fungi (three strains), and bacteria (nine strains) in vitro. Charcoal, dolomite powder, gypsum, and phosphogypsum did not suppress the growth either of microfungi or of bacteria in the tested substrate, and even acted as promoters of their growth. The effects of the other agents tested were strain dependent. Potassium permanganate could be used for bacteria and Candida spp. growth suppression, but not for other microfungi. Lime showed promising results by suppressing the growth of microfungi and bacteria, but it also suppressed the growth of white-rot fungi. Hydrogen peroxide showed strong suppression of microfungi, and even had a bactericidal effect on some bacteria, but did not have an impact on white-rot fungi. The study highlights the practical utility of using hydrogen peroxide up to 3% as an effective biota-suppressing chemical agent prior to inoculating white-rot fungi in the large-scale bioremediation of polluted substrates, or in the large-scale cultivation for mushroom production as a foodstuff. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>The significance of the work and experimental design.</p> Full article ">Figure 2
<p>Suppression level of white-rot fungi <span class="html-italic">Pleurotus ostreatus</span> by the action of different concentrations of KMnO<sub>4</sub> and NaOH.</p> Full article ">
22 pages, 5205 KiB  
Article
Sigma Factor Engineering in Actinoplanes sp. SE50/110: Expression of the Alternative Sigma Factor Gene ACSP50_0507 (σHAs) Enhances Acarbose Yield and Alters Cell Morphology
by Laura Schlüter, Tobias Busche, Laila Bondzio, Andreas Hütten, Karsten Niehaus, Susanne Schneiker-Bekel, Alfred Pühler and Jörn Kalinowski
Microorganisms 2024, 12(6), 1241; https://doi.org/10.3390/microorganisms12061241 - 20 Jun 2024
Cited by 2 | Viewed by 1674
Abstract
Sigma factors are transcriptional regulators that are part of complex regulatory networks for major cellular processes, as well as for growth phase-dependent regulation and stress response. Actinoplanes sp. SE50/110 is the natural producer of acarbose, an α-glucosidase inhibitor that is used in diabetes [...] Read more.
Sigma factors are transcriptional regulators that are part of complex regulatory networks for major cellular processes, as well as for growth phase-dependent regulation and stress response. Actinoplanes sp. SE50/110 is the natural producer of acarbose, an α-glucosidase inhibitor that is used in diabetes type 2 treatment. Acarbose biosynthesis is dependent on growth, making sigma factor engineering a promising tool for metabolic engineering. ACSP50_0507 is a homolog of the developmental and osmotic-stress-regulating Streptomyces coelicolor σHSc. Therefore, the protein encoded by ACSP50_0507 was named σHAs. Here, an Actinoplanes sp. SE50/110 expression strain for the alternative sigma factor gene ACSP50_0507 (sigHAs) achieved a two-fold increased acarbose yield with acarbose production extending into the stationary growth phase. Transcriptome sequencing revealed upregulation of acarbose biosynthesis genes during growth and at the late stationary growth phase. Genes that are transcriptionally activated by σHAs frequently code for secreted or membrane-associated proteins. This is also mirrored by the severely affected cell morphology, with hyperbranching, deformed and compartmentalized hyphae. The dehydrated cell morphology and upregulation of further genes point to a putative involvement in osmotic stress response, similar to its S. coelicolor homolog. The DNA-binding motif of σHAs was determined based on transcriptome sequencing data and shows high motif similarity to that of its homolog. The motif was confirmed by in vitro binding of recombinantly expressed σHAs to the upstream sequence of a strongly upregulated gene. Autoregulation of σHAs was observed, and binding to its own gene promoter region was also confirmed. Full article
(This article belongs to the Special Issue Transcriptional Regulation in Bacteria)
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<p><span class="html-italic">Actinoplanes</span> sp. SE50/110 ACSP50_0507 protein sequence and domain annotation. Sequence alignment to σH (SCO5243) of <span class="html-italic">S. coelicolor</span> performed with Clustal Omega [<a href="#B78-microorganisms-12-01241" class="html-bibr">78</a>] and domains were predicted using InterPro [<a href="#B78-microorganisms-12-01241" class="html-bibr">78</a>,<a href="#B79-microorganisms-12-01241" class="html-bibr">79</a>]. ACSP50_0507 has a σ B/F/G family protein domain, containing three regions of different functions. Region 2 is an RNA-polymerase binding and −10 promoter recognition region, region 3 is an RNA-polymerase and extended −10-binding region and region 4 is a −35 promoter element-recognition region where DNA-binding residues are depicted in a lighter color. “*” indicates identical residues, “:“ indicates conservation between groups of strongly similar properties and “.” indicates conservation between groups of weakly similar properties.</p> Full article ">Figure 2
<p>Scanning electron microscopy (SEM) of <span class="html-italic">Actinoplanes</span> sp. SE50/110. Strains were cultivated in maltose minimal medium for 5 days before sample preparation for microscopic imaging. The wild-type (<b>a</b>) and empty vector control strain pSETT4<span class="html-italic">tipA</span> (<b>c</b>) grow as large, dense mycelium, surrounded by a large web of hyphae. Hyphae of the wild-type (<b>b</b>) and pSETT4<span class="html-italic">tipA</span> (<b>d</b>) have a uniform shape and are occasionally branched. The morphology of the <span class="html-italic">sigH</span><sup>As</sup> expression strain is drastically influenced to a dense mycelium without a hyphal mesh surrounding it (<b>e</b>). Hyphae are deformed, with an irregular shape (<b>f</b>,<b>g</b>), and closer magnification of the blue marked area shows a highly wrinkled cell surface (<b>h</b>).</p> Full article ">Figure 3
<p>Characterization of growth and acarbose production of <span class="html-italic">Actinoplanes</span> sp. SE50/110 <span class="html-italic">sigH</span><sup>As</sup> expression and control strains. (<b>a</b>) Cell dry weight (dense lines) and acarbose concentration (bracket lines) from supernatant during cultivation in maltose minimal medium are shown for <span class="html-italic">Actinoplanes</span> sp. SE50/110 wild type, empty vector strain pSETT4<span class="html-italic">tipA</span> and σH<sup>As</sup> expression strain pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup> (n = 3). (<b>b</b>) Optimal biomass-related acarbose yield of <span class="html-italic">Actinoplanes</span> sp. SE50/110 wild type, empty vector strain pSETT4<span class="html-italic">tipA</span> and expression strain pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup> at 166 h of cultivation (n = 3).</p> Full article ">Figure 4
<p>Relative <span class="html-italic">sigH</span><sup>As</sup> transcript amounts of <span class="html-italic">sigH</span><sup>As</sup> expression, deletion and complementation strains. RNA was isolated from cell pellet of 1 mL of cultivation sample at 96 h of cultivation in maltose minimal medium. RT-qPCR was performed for three biological replicates per condition with two technical replicates each. (<b>a</b>) <span class="html-italic">sigH</span><sup>As</sup> overexpression detected for the chromosomal and ectopic <span class="html-italic">sigH</span><sup>As</sup> gene copy of the expression strain (pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup>) compared to the empty vector control strain. The gene expression strain (pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup>) has a 2.5-fold increased transcript level. (<b>b</b>) Repression of transcription of the chromosomal gene copy. The expression strain (pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup>) was compared to the empty vector control strain with 5′UTR specific oligonucleotides to differentiate between chromosomal and ectopic gene copy. The genomic integrated additional gene copy must be highly transcribed to achieve 2.5-fold upregulation detected for chromosomal and ectopic gene copy, despite the downregulation of the chromosomal gene copy. (<b>c</b>) Transcription of the deletion strain ∆<span class="html-italic">sigH</span><sup>As</sup> was compared to <span class="html-italic">Actinoplanes</span> sp. SE50/110 wild type and is at the lower limit of detection. (<b>d</b>) Gene transcription of the complementation strain (C∆<span class="html-italic">sigH</span><sup>As</sup>) was compared to the empty vector control strain. Asterisks indicate <span class="html-italic">p</span>-value of a two-sided <span class="html-italic">t</span>-test with n.s. <span class="html-italic">p</span> ≥ 0.05, * <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> Full article ">Figure 5
<p>Differential transcription of <span class="html-italic">acb</span> genes. Transcriptome sequencing data of expression strain pSETT4<span class="html-italic">tipA</span>-<span class="html-italic">sigH</span><sup>As</sup> compared to the empty vector control strain pSETT4<span class="html-italic">tipA</span> during growth. (<b>a</b>) Average differential <span class="html-italic">acb</span> gene transcription and standard deviation of <span class="html-italic">sigH</span><sup>As</sup> expression strain is shown in dark blue. Minimal and maximal differential gene transcription is shown within the colored area, defined by red (maximal) or black (minimal) lines. (<b>b</b>) Differential gene transcription of single <span class="html-italic">acb</span> genes is shown as a heatmap with the respective M-values within the color-marked areas. Arrows indicate operon structure and gene orientation.</p> Full article ">Figure 6
<p>σH<sup>As</sup> binding motif prediction. (<b>a</b>) Venn diagram of significantly upregulated genes (M-value ≥1; P<sub>adj</sub> &lt; 0.05) of the <span class="html-italic">sigH</span><sup>As</sup> expression mutant from growth to late stationary phase (96 h, 142 h, 118 h, 166 h). (<b>b</b>) Predicted binding motif of −35 and −10 region and transcription start site (TSS) for σH<sup>As</sup>. The motif was predicted based on upstream sequences (−60 to +10 nt) according to the TSS, determined by 5′-end specific transcript sequencing. The analysis was performed with Improbizer [<a href="#B61-microorganisms-12-01241" class="html-bibr">61</a>] and visualized with WebLogo 3.7.10 [<a href="#B60-microorganisms-12-01241" class="html-bibr">60</a>] for 178 upstream sequences of permanently upregulated genes. (<b>c</b>) Determined binding motif of the homologous σH<sup>Sc</sup> of <span class="html-italic">S. coelicolor</span>, based on 11 known binding sites [<a href="#B43-microorganisms-12-01241" class="html-bibr">43</a>,<a href="#B46-microorganisms-12-01241" class="html-bibr">46</a>].</p> Full article ">Figure 7
<p>Electrophoretic mobility shift assay (EMSA) with recombinantly expressed C-terminal His<sub>6</sub> tagged σH<sup>As</sup>. Binding was tested for upstream region of ACSP50_4135 and its own promoter. All binding reactions contained Poly (dI-dC) as a non-specific competitor and 0.05 nM of the respective biotinylated dsDNA fragment of 61 base pairs. To the reaction, 3.40 uM of protein and 0.05–1 nM of specific unlabeled competitor DNA were either added (+) or not (−). Filled arrows indicate biotinylated DNA and open arrows indicate the shifted protein–DNA complex. (<b>a</b>) EMSA with the promoter region (−60 to +1) of ACSP50_4135 confirms specific binding by σH<sup>As</sup>-His<sub>6</sub>. (<b>b</b>) Binding to the promoter region (−60 to +1) of <span class="html-italic">sigH</span><sup>As</sup> was also confirmed. (<b>c</b>) σH<sup>As</sup>-His6 does not show unspecific binding to DNA, which was confirmed by a lack of binding to the intragenic region of luciferase DNA (GenBank: EU239244.1).</p> Full article ">
16 pages, 6421 KiB  
Article
Upper Limb Compartment Syndrome—An Extremely Rare Life-Threatening Complication of Cutaneous Anthrax
by Mihaela Pertea, Stefana Luca, Dan Cristian Moraru, Bogdan Veliceasa, Alexandru Filip, Oxana Madalina Grosu, Vladimir Poroch, Andrian Panuta, Catalina Mihaela Luca, Andrei Nicolae Avadanei and Sorinel Lunca
Microorganisms 2024, 12(6), 1240; https://doi.org/10.3390/microorganisms12061240 - 20 Jun 2024
Cited by 1 | Viewed by 1558
Abstract
(1) Background: Cutaneous anthrax is a disease caused by a Gram-positive bacillus, spore-forming Bacillus anthracis (BA). Cutaneous anthrax accounts for 95% of all anthrax cases, with mortality between 10–40% in untreated forms. The most feared complication, which can be life-threatening and is rarely [...] Read more.
(1) Background: Cutaneous anthrax is a disease caused by a Gram-positive bacillus, spore-forming Bacillus anthracis (BA). Cutaneous anthrax accounts for 95% of all anthrax cases, with mortality between 10–40% in untreated forms. The most feared complication, which can be life-threatening and is rarely encountered and described in the literature, is compartment syndrome. (2) Methods: We report a series of six cases of cutaneous anthrax from the same endemic area. In two of the cases, the disease was complicated by compartment syndrome. The systematic review was conducted according to systematic review guidelines, and the PubMed, Google Scholar, and Web of Science databases were searched for publications from 1 January 2008 to 31 December 2023. The keywords used were: “cutaneous anthrax” and “compartment syndrome by cutaneous anthrax”. (3) Results: For compartment syndrome, emergency surgical intervention for decompression was required, along with another three surgeries, with hospitalization between 21 and 23 days. In the systematic review, among the 37 articles, 29 did not contain cases focusing on compartment syndrome of the thoracic limb in cutaneous anthrax. The results were included in a Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram. (4) Conclusions: Early recognition of the characteristic cutaneous lesions and compartment syndrome with early initiation of antibiotics and urgent surgical treatment is the lifesaving solution. Full article
(This article belongs to the Section Medical Microbiology)
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<p>(<b>a</b>) Right forearm cutaneous anthrax lesion, (<b>b</b>) lesion detail.</p> Full article ">Figure 2
<p>Initial left forearm injury—day 1.</p> Full article ">Figure 3
<p>(<b>a</b>) Cutaneous anthrax case 1 with compartment syndrome, (<b>b</b>) cutaneous anthrax case 1 with compartment syndrome—detail, (<b>c</b>) decompression incisions for surgical treatment of compartment syndrome; significant erythema and edema at the level of the left thoracic limb.</p> Full article ">Figure 4
<p>(<b>a</b>) Failure of split skin graft, (<b>b</b>) direct closure of the decompression incisions after edema remission.</p> Full article ">Figure 5
<p>The progressive but self-limited evolution of the injury in the right forearm. (<b>a</b>) The 5th day of evolution, (<b>b</b>) the 20th day of evolution.</p> Full article ">Figure 6
<p><span class="html-italic">Bacillus anthracis</span> culture.</p> Full article ">Figure 7
<p>Few Gram-positive bacilli with truncated ends in a case 1 (scale bar: 10 μm).</p> Full article ">Figure 8
<p>(<b>a</b>) Pustule (HEx40) scale bar: 250 μm, (<b>b</b>) necrosis with hypodermic thrombosis (HEx40) scale bar: 250 μm, (<b>c</b>) periaxial inflammation (HEx100) scale bar: 100 μm.</p> Full article ">Figure 9
<p>Cutaneous anthrax. Characteristic skin lesions evolving within a 3-day interval. (<b>a</b>) Aspect on admission, (<b>b</b>) aspect on the 2nd day after admission, (<b>c</b>) 3rd day after admission, with the onset of compartment syndrome and the appearance of characteristic lesions for cutaneous anthrax.</p> Full article ">Figure 10
<p>(<b>a</b>). Decompression incisions in compartment syndrome. (<b>b</b>) Closing and covering the postexcisional soft tissue defect by plasty with split skin graft after edema remission.</p> Full article ">Figure 11
<p>Gram-positive bacilli with truncated ends in a case 2 scale bar: 100 μm.</p> Full article ">Figure 12
<p>(<b>a</b>) Epithelium with coagulation necrosis (HEx100) scale bar: 100 μm, (<b>b</b>) dermis with coagulation necrosis (HEx40) scale bar: 250 μm, and (<b>c</b>) hypodermis with coagulation necrosis (HEx100) scale bar: 100 μm.</p> Full article ">Figure 13
<p>(<b>a</b>). Multiple self-limited cutaneous anthrax lesions on the left forearm, (<b>b</b>) single cutaneous anthrax lesion on the dorsal aspect of the right forearm after an initial excision.</p> Full article ">Figure 14
<p>The flowchart of articles selected for the review (n = number).</p> Full article ">
14 pages, 449 KiB  
Article
Effect of Triticale Grain in Diets on Performance, Development of Gastrointestinal Tract and Microflora in Crop and Ileum of Broiler Chickens
by Patrycja Wróblewska, Tomasz Hikawczuk, Anna Szuba-Trznadel, Andrzej Wiliczkiewicz, Andrii Zinchuk, Agnieszka Rusiecka and Krystyna Laszki-Szcząchor
Microorganisms 2024, 12(6), 1239; https://doi.org/10.3390/microorganisms12061239 - 20 Jun 2024
Viewed by 1012
Abstract
The purpose of the research was to determine the effect of the use of a diet containing 30% triticale grain. In an experiment lasting 28 days, 180 one-day Ross-308 chickens (sex ratio 1:1) with an average initial body weight in treatment of 44.6 [...] Read more.
The purpose of the research was to determine the effect of the use of a diet containing 30% triticale grain. In an experiment lasting 28 days, 180 one-day Ross-308 chickens (sex ratio 1:1) with an average initial body weight in treatment of 44.6 g were randomly assigned to 30 metabolic cages/replications, 6 birds in each. To compare the results between treatments, a one-way ANOVA was used with uneven replication numbers. The control group (I) received a standard diet containing maize and soybean meal. In the other treatments, 30% of different cereals were used: II—wheat, III—barley, and IV—triticale. Significant differences in body weight (BW) and feed conversion ratio (FCR) were observed on the 4th day of the life of broiler chickens (p < 0.05). Differences were determined between the control group (90.7 g BW and 1.32 kg of feed/kg BWG in the case of FCR) and birds receiving barley (93.0 g BW and 1.29 kg of feed/kg BWG in the case of FCR), compared to chickens fed diets with a 30% share of wheat grain (86.2 g BW and 1.53 kg feed/kg BWG in the case of FCR) and triticale (86.6 g BW and 1.53 kg feed/kg BWG in the case of FCR). Later, the differences in performance of birds between treatments did not occur (p > 0.05). In the nutrition of broiler chickens, control or 30% of the triticale diet caused a significant reduction (p < 0.01) of the number of Escherichia coli (E. coli) in the crop of broiler chickens (0 log cfu/g), compared to birds obtaining feed with 30% of wheat (1.78 log cfu/g). The diet containing triticale also reduced the number of E. coli (p < 0.05) within the ileum (0.78 log cfu/g) compared to chickens obtaining barley grain in the diet (2.12 log cfu/g). As a result of the use of triticale grain (p < 0.05), the total length of the bird intestines (199.64 cm) was compared to 30% of barley grain (209.76 cm). The increase in the length of the large intestine of broiler chickens in treatments was positively correlated (r = 0.613, p < 0.05) with the number of Lactobacillus sp. in the ileum. Triticale increased the pH in the crop of broilers chickens. The research results indicate that triticale, after longer storage, can be used in amounts of 30% of the diet without significant effect on the performance of broiler chickens, with a reduction in E. coli in crop in comparison with wheat and in ileum with barley. Full article
(This article belongs to the Section Veterinary Microbiology)
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<p>Effect of diet type on the pH of crop and ileum. Means in labels with different superscripts A, B significant at <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">
21 pages, 1668 KiB  
Article
The Usefulness of the C2HEST Score in Predicting the Clinical Outcomes of COVID-19 in COPD and Non-COPD Cohorts
by Jakub Gawryś, Adrian Doroszko, Olgierd Dróżdż, Małgorzata Trocha, Damian Gajecki, Karolina Gawryś, Ewa Szahidewicz-Krupska, Maciej Rabczyński, Krzysztof Kujawa, Piotr Rola, Agata Stanek, Janusz Sokołowski, Marcin Madziarski, Ewa Anita Jankowska, Agnieszka Bronowicka-Szydełko, Dorota Bednarska-Chabowska, Edwin Kuźnik and Katarzyna Madziarska
Microorganisms 2024, 12(6), 1238; https://doi.org/10.3390/microorganisms12061238 - 20 Jun 2024
Viewed by 1671
Abstract
Patients with chronic obstructive pulmonary disease (COPD) infected with SARS-CoV-2 indicate a higher risk of severe COVID-19 course, which is defined as the need for hospitalization in the intensive care unit, mechanical ventilation, or death. However, simple tools to stratify the risk in [...] Read more.
Patients with chronic obstructive pulmonary disease (COPD) infected with SARS-CoV-2 indicate a higher risk of severe COVID-19 course, which is defined as the need for hospitalization in the intensive care unit, mechanical ventilation, or death. However, simple tools to stratify the risk in patients with COPD suffering from COVID-19 are lacking. The current study aimed to evaluate the predictive value of the C2HEST score in patients with COPD. A retrospective analysis of medical records from 2184 patients hospitalized with COVID-19 at the University Hospital in Wroclaw from February 2020 to June 2021, which was previously used in earlier studies, assessed outcomes such as mortality during hospitalization, all-cause mortality at 3 and 6 months, non-fatal discharge, as well as adverse clinical incidents. This re-analysis specifically examines the outcomes using a COPD split. In the COPD group, 42 deaths were recorded, including 18 in-hospital deaths. In-hospital mortality rates at 3 and 6 months did not significantly differ among C2HEST strata, nor did their impact on subsequent treatment. However, a notable association between the C2HEST score and prognosis was observed in the non-COPD cohort comprising 2109 patients. The C2HEST score’s predictive ability is notably lower in COPD patients compared to non-COPD subjects, with COPD itself indicating a high mortality risk. However, C2HEST effectively identifies patients at high risk of cardiac complications during COVID-19, especially in non-COPD cases. Full article
(This article belongs to the Special Issue Advances in SARS-CoV-2 Infection—Third Edition)
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<p>ROC curves for the C<sub>2</sub>HEST score in predicting total mortality in the study groups: COPD (<b>A</b>–<b>C</b>) and control non-COPD groups (<b>D</b>–<b>F</b>) at selected time points (30 days; 90 days; and 180 days) after the positive RT-PCR test. Abbreviations: area under the curve—AUC; receiver operating characteristic—ROC.</p> Full article ">Figure 2
<p>Time-dependent ROC analysis for the C<sub>2</sub>HEST predictive values of all-cause death in the study (<b>A</b>) and control (<b>B</b>) groups (mean with CI). Abbreviations: area under the curve—AUC; receiver operating characteristic—ROC.</p> Full article ">Figure 3
<p>Time-dependent ROC analysis for the C<sub>2</sub>HEST predictive values of in-hospital all-cause death in the study (<b>A</b>) and control (<b>B</b>) groups (mean with CI). Abbreviations: area under the curve—AUC; receiver operating characteristic—ROC.</p> Full article ">Figure 4
<p>Analysis of 6-month survival across low, medium, and high C<sub>2</sub>HEST risk categories in the study (<b>A</b>) and control (<b>B</b>) groups (mean with CI). Abbreviation: area under the curve—AUC.</p> Full article ">
13 pages, 3026 KiB  
Systematic Review
Canine Bacterial Endocarditis: A Text Mining and Topics Modeling Analysis as an Approach for a Systematic Review
by Annalisa Previti, Vito Biondi, Annamaria Passantino, Mehmet Erman Or and Michela Pugliese
Microorganisms 2024, 12(6), 1237; https://doi.org/10.3390/microorganisms12061237 - 19 Jun 2024
Cited by 1 | Viewed by 1437
Abstract
Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study [...] Read more.
Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus® database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed. Full article
(This article belongs to the Section Veterinary Microbiology)
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<p>Flow diagram of the review process according to the PRISMA statement.</p> Full article ">Figure 2
<p>The total number of records published per year between 1950 and 2023.</p> Full article ">Figure 3
<p>Five journals more representative for the publication of articles related to the topic.</p> Full article ">Figure 4
<p>The histogram reports in the root the most frequently words used based on the weighting system (TF-IDF ≥ 0.8).</p> Full article ">Figure 5
<p>The word cloud reports the words more frequently used. The font size corresponds to the TF-IDF value of each word.</p> Full article ">Figure 6
<p>Topics numbered from 1 to 5 according to the cumulative probabilities (CPs), as well as the first 10 words for each topic numbered.</p> Full article ">Figure 7
<p>Distribution of the articles within the five topics from 1950 to 2024.</p> Full article ">
13 pages, 2045 KiB  
Article
Hunting Dynamics and Identification of Potentially Pathogenic Bacteria in European Fallow Deer (Dama dama) across Three Hunting Reserves in Western Romania
by Iulia-Maria Bucur, Alex Cristian Moza, Mirel Pop, Ileana Nichita, Cristina Mirabela Gaspar, Răzvan Cojocaru, Radu-Valentin Gros, Marius Valentin Boldea, Andreea Tirziu and Emil Tirziu
Microorganisms 2024, 12(6), 1236; https://doi.org/10.3390/microorganisms12061236 - 19 Jun 2024
Cited by 1 | Viewed by 854
Abstract
The study focused on the hunting practices and potentially pathogenic bacterial species among European fallow deer (Dama dama). Within a five-year period, three hunting grounds from Western Romania were examined. During this period, a total of 1881 deer were hunted, and [...] Read more.
The study focused on the hunting practices and potentially pathogenic bacterial species among European fallow deer (Dama dama). Within a five-year period, three hunting grounds from Western Romania were examined. During this period, a total of 1881 deer were hunted, and 240 samples were collected by rectal and nasal swabbing from 120 carcasses. Bacterial strains were identified utilizing bacteriological assays and the Vitek® 2 Compact system. Notably, the Socodor hunting ground exhibited a significant difference in harvesting quotas between the bucks (Group M) and does/yearlings (Group F), favoring the latter. In the Chișineu Criș–Sălișteanca hunting ground, a likely correlation in harvesting quotas between the two groups was observed. The identified potentially pathogenic bacteria were Escherichia coli, Salmonella spp., Staphylococcus aureus, Listeria monocytogenes and Enterococcus faecium. These results highlight the importance of effectively managing the deer population and recognize the potential for Dama dama to spread zoonotic pathogens, emphasizing the necessity of adopting a One Health approach and maintaining ongoing surveillance of this game species’ population dynamics. Full article
(This article belongs to the Special Issue Domestic Animals and Wildlife Zoonotic Microorganisms)
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<p>The sites of the hunting grounds, Socodor (A), Chișineu Criș–Sălișteanca (B) (Arad county), and Nadăș (C) (Timiș county). Map source: Google Maps.</p> Full article ">Figure 2
<p>A comparison in harvesting quota dynamics between 2017 and 2021 in Hunting Ground Socodor: (<b>a</b>) Group M—ascending trend; (<b>b</b>) Group F—slight descending trend.</p> Full article ">Figure 3
<p>A comparison in harvesting quota dynamics between 2017 and 2021 in Hunting Ground Chișineu Criș–Sălișteanca: (<b>a</b>) Group M—slightly ascending trend; (<b>b</b>) Group F—slightly ascending trend.</p> Full article ">Figure 4
<p>Relationship between the harvesting quota dynamics of Group M and Group F between 2017 and 2021: (<b>a</b>) in Hunting Ground A; (<b>b</b>) in Hunting Ground B.</p> Full article ">
14 pages, 3191 KiB  
Review
The Role of Short-Chain Fatty Acids, Particularly Butyrate, in Oncological Immunotherapy with Checkpoint Inhibitors: The Effectiveness of Complementary Treatment with Clostridium butyricum 588
by Massimiliano Cazzaniga, Marco Cardinali, Francesco Di Pierro, Giordano Bruno Zonzini, Chiara Maria Palazzi, Aurora Gregoretti, Nicola Zerbinati, Luigina Guasti, Maria Rosaria Matera, Ilaria Cavecchia and Alexander Bertuccioli
Microorganisms 2024, 12(6), 1235; https://doi.org/10.3390/microorganisms12061235 - 19 Jun 2024
Cited by 2 | Viewed by 2317
Abstract
The discovery of immune checkpoints (CTLA-4, PD-1, and PD-L1) and their impact on the prognosis of oncological diseases have paved the way for the development of revolutionary oncological treatments. These treatments do not combat tumors with drugs “against” cancer cells but rather support [...] Read more.
The discovery of immune checkpoints (CTLA-4, PD-1, and PD-L1) and their impact on the prognosis of oncological diseases have paved the way for the development of revolutionary oncological treatments. These treatments do not combat tumors with drugs “against” cancer cells but rather support and enhance the ability of the immune system to respond directly to tumor growth by attacking the cancer cells with lymphocytes. It has now been widely demonstrated that the presence of an adequate immune response, essentially represented by the number of TILs (tumor-infiltrating lymphocytes) present in the tumor mass decisively influences the response to treatments and the prognosis of the disease. Therefore, immunotherapy is based on and cannot be carried out without the ability to increase the presence of lymphocytic cells at the tumor site, thereby limiting and nullifying certain tumor evasion mechanisms, particularly those expressed by the activity (under positive physiological conditions) of checkpoints that restrain the response against transformed cells. Immunotherapy has been in the experimental phase for decades, and its excellent results have made it a cornerstone of treatments for many oncological pathologies, especially when combined with chemotherapy and radiotherapy. Despite these successes, a significant number of patients (approximately 50%) do not respond to treatment or develop resistance early on. The microbiota, its composition, and our ability to modulate it can have a positive impact on oncological treatments, reducing side effects and increasing sensitivity and effectiveness. Numerous studies published in high-ranking journals confirm that a certain microbial balance, particularly the presence of bacteria capable of producing short-chain fatty acids (SCFAs), especially butyrate, is essential not only for reducing the side effects of chemoradiotherapy treatments but also for a better response to immune treatments and, therefore, a better prognosis. This opens up the possibility that favorable modulation of the microbiota could become an essential complementary treatment to standard oncological therapies. This brief review aims to highlight the key aspects of using precision probiotics, such as Clostridium butyricum, that produce butyrate to improve the response to immune checkpoint treatments and, thus, the prognosis of oncological diseases. Full article
(This article belongs to the Section Gut Microbiota)
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<p>Mechanisms of tumor evasion from the immune system: inhibition of APCs, secretion of immunosuppressive factors (e.g., TGF-β), inhibition of previously activated cells, and recruitment of Tregs.</p> Full article ">Figure 2
<p>Synthesis of butyrate from carbohydrates with low or limited digestibility through fermentation mediated by intestinal bacteria [<a href="#B19-microorganisms-12-01235" class="html-bibr">19</a>].</p> Full article ">Figure 3
<p>Microbiome immunomodulation through innate and adaptive immunity [<a href="#B39-microorganisms-12-01235" class="html-bibr">39</a>].</p> Full article ">Figure 4
<p>Modulation mechanism of the microbiota on CPI efficacy through CD8+ maturation, TH1 regulation with increased levels of IFN-γ, and Treg regulation, consequently activating effector T cells [<a href="#B48-microorganisms-12-01235" class="html-bibr">48</a>].</p> Full article ">
24 pages, 7844 KiB  
Article
Diagnostic Performance of a Molecular Assay in Synovial Fluid Targeting Dominant Prosthetic Joint Infection Pathogens
by Jiyoung Lee, Eunyoung Baek, Hyesun Ahn, Heechul Park, Suchan Lee and Sunghyun Kim
Microorganisms 2024, 12(6), 1234; https://doi.org/10.3390/microorganisms12061234 - 19 Jun 2024
Viewed by 1257
Abstract
Prosthetic joint infection (PJI) is one of the most serious complications of joint replacement surgery among orthopedic surgeries and occurs in 1 to 2% of primary surgeries. Additionally, the cause of PJIs is mostly bacteria from the Staphylococcus species, accounting for more than [...] Read more.
Prosthetic joint infection (PJI) is one of the most serious complications of joint replacement surgery among orthopedic surgeries and occurs in 1 to 2% of primary surgeries. Additionally, the cause of PJIs is mostly bacteria from the Staphylococcus species, accounting for more than 98%, while fungi cause PJIs in only 1 to 2% of cases and can be difficult to manage. The current gold-standard microbiological method of culturing synovial fluid is time-consuming and produces false-negative and -positive results. This study aimed to identify a novel, accurate, and convenient molecular diagnostic method. The DreamDX primer–hydrolysis probe set was designed for the pan-bacterial and pan-fungal detection of DNA from pathogens that cause PJIs. The sensitivity and specificity of DreamDX primer–hydrolysis probes were 88.89% (95% CI, 56.50–99.43%) and 97.62% (95% CI, 87.68–99.88%), respectively, compared with the microbiological method of culturing synovial fluid, and receiver operating characteristic (ROC) area under the curve (AUC) was 0.9974 (*** p < 0.0001). It could be concluded that the DreamDX primer–hydrolysis probes have outstanding potential as a molecular diagnostic method for identifying the causative agents of PJIs, and that host inflammatory markers are useful as adjuvants in the diagnosis of PJIs. Full article
(This article belongs to the Special Issue Molecular Techniques, Novel Biomarkers and Coagulation-Based Methods in the Diagnosis of Periprosthetic Joint Infections)
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<p>Sequences and positions of (<b>A</b>) the nine dominant PJI bacteria-specific PCR primers and probes designed and used in this study for the 16S rRNA gene and (<b>B</b>) the four dominant PJI fungi-specific PCR primers and probes designed and used in this study for the 18S rRNA gene. Forward and reverse primer sequences are indicated with red frames, while probe sequences are indicated with green frames. The “*” character means that the residues or nucleotides are indicated identical conserved.</p> Full article ">Figure 1 Cont.
<p>Sequences and positions of (<b>A</b>) the nine dominant PJI bacteria-specific PCR primers and probes designed and used in this study for the 16S rRNA gene and (<b>B</b>) the four dominant PJI fungi-specific PCR primers and probes designed and used in this study for the 18S rRNA gene. Forward and reverse primer sequences are indicated with red frames, while probe sequences are indicated with green frames. The “*” character means that the residues or nucleotides are indicated identical conserved.</p> Full article ">Figure 2
<p>Amplification curves and standard curves of target plasmids with concentrations of 1 × 10<sup>8</sup> copies/μL to 1 × 10<sup>1</sup> copies/μL. (<b>A</b>) <span class="html-italic">Staphylococcus epidermidis</span>, (<b>B</b>) <span class="html-italic">Staphylococcus aureus</span>, (<b>C</b>) <span class="html-italic">Enterococcus faecalis</span>, (<b>D</b>) <span class="html-italic">Streptococcus dysgalactiae</span>, (<b>E</b>) <span class="html-italic">Streptococcus pyogenes</span>, (<b>F</b>) <span class="html-italic">Escherichia coli</span>, (<b>G</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>H</b>) <span class="html-italic">Pseudomonas aeruginosa</span>, (<b>I</b>) <span class="html-italic">Enterobacter cloacae</span>, (<b>J</b>) <span class="html-italic">Candida glabrata</span>, (<b>K</b>) <span class="html-italic">Candida albicans</span>, (<b>L</b>) <span class="html-italic">Candida tropicalis</span>, and (<b>M</b>) <span class="html-italic">Candida parapsilosis</span>.</p> Full article ">Figure 2 Cont.
<p>Amplification curves and standard curves of target plasmids with concentrations of 1 × 10<sup>8</sup> copies/μL to 1 × 10<sup>1</sup> copies/μL. (<b>A</b>) <span class="html-italic">Staphylococcus epidermidis</span>, (<b>B</b>) <span class="html-italic">Staphylococcus aureus</span>, (<b>C</b>) <span class="html-italic">Enterococcus faecalis</span>, (<b>D</b>) <span class="html-italic">Streptococcus dysgalactiae</span>, (<b>E</b>) <span class="html-italic">Streptococcus pyogenes</span>, (<b>F</b>) <span class="html-italic">Escherichia coli</span>, (<b>G</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>H</b>) <span class="html-italic">Pseudomonas aeruginosa</span>, (<b>I</b>) <span class="html-italic">Enterobacter cloacae</span>, (<b>J</b>) <span class="html-italic">Candida glabrata</span>, (<b>K</b>) <span class="html-italic">Candida albicans</span>, (<b>L</b>) <span class="html-italic">Candida tropicalis</span>, and (<b>M</b>) <span class="html-italic">Candida parapsilosis</span>.</p> Full article ">Figure 2 Cont.
<p>Amplification curves and standard curves of target plasmids with concentrations of 1 × 10<sup>8</sup> copies/μL to 1 × 10<sup>1</sup> copies/μL. (<b>A</b>) <span class="html-italic">Staphylococcus epidermidis</span>, (<b>B</b>) <span class="html-italic">Staphylococcus aureus</span>, (<b>C</b>) <span class="html-italic">Enterococcus faecalis</span>, (<b>D</b>) <span class="html-italic">Streptococcus dysgalactiae</span>, (<b>E</b>) <span class="html-italic">Streptococcus pyogenes</span>, (<b>F</b>) <span class="html-italic">Escherichia coli</span>, (<b>G</b>) <span class="html-italic">Acinetobacter baumannii</span>, (<b>H</b>) <span class="html-italic">Pseudomonas aeruginosa</span>, (<b>I</b>) <span class="html-italic">Enterobacter cloacae</span>, (<b>J</b>) <span class="html-italic">Candida glabrata</span>, (<b>K</b>) <span class="html-italic">Candida albicans</span>, (<b>L</b>) <span class="html-italic">Candida tropicalis</span>, and (<b>M</b>) <span class="html-italic">Candida parapsilosis</span>.</p> Full article ">Figure 3
<p>Amplification plots of qPCR for nine bacterial and four fungal reference strains. <span class="html-italic">X</span>-axis: cycle numbers; <span class="html-italic">Y</span>-axis: relative fluorescence units (RFU). (<b>A</b>) Amplification curves generated using the pan-bacterial DreamDX primer–probe set for positive samples (<span class="html-italic">Staphylococcus epidermidis</span>, <span class="html-italic">Staphylococcus aureus</span>, <span class="html-italic">Enterococcus faecalis</span>, <span class="html-italic">Streptococcus dysgalactiae</span>, <span class="html-italic">Streptococcus pyogenes</span>, <span class="html-italic">Escherichia coli</span>, <span class="html-italic">Acinetobacter baumannii</span>, <span class="html-italic">Pseudomonas aeruginosa</span>, and <span class="html-italic">Enterobacter cloacae</span>), negative samples (<span class="html-italic">Candida parapsilosis</span>, <span class="html-italic">Candida tropicalis</span>, <span class="html-italic">Candida glabrata</span>, and <span class="html-italic">Candida albicans</span>), and distilled water used as a no-template control (NTC). (<b>B</b>) Amplification curves generated using the pan-fungal DreamDX primer–probe set for positive samples (<span class="html-italic">C. parapsilosis</span>, <span class="html-italic">C. tropicalis</span>, <span class="html-italic">C. glabrata</span>, and <span class="html-italic">C. albicans</span>), negative samples (<span class="html-italic">S. epidermidis</span>, <span class="html-italic">S. aureus</span>, <span class="html-italic">E. faecalis</span>, <span class="html-italic">S. dysgalactiae</span>, <span class="html-italic">S. pyogenes</span>, <span class="html-italic">E. coli</span>, <span class="html-italic">A. baumannii</span>, <span class="html-italic">P. aeruginosa</span>, and <span class="html-italic">E. cloacae</span>), and distilled water used as an NTC.</p> Full article ">Figure 3 Cont.
<p>Amplification plots of qPCR for nine bacterial and four fungal reference strains. <span class="html-italic">X</span>-axis: cycle numbers; <span class="html-italic">Y</span>-axis: relative fluorescence units (RFU). (<b>A</b>) Amplification curves generated using the pan-bacterial DreamDX primer–probe set for positive samples (<span class="html-italic">Staphylococcus epidermidis</span>, <span class="html-italic">Staphylococcus aureus</span>, <span class="html-italic">Enterococcus faecalis</span>, <span class="html-italic">Streptococcus dysgalactiae</span>, <span class="html-italic">Streptococcus pyogenes</span>, <span class="html-italic">Escherichia coli</span>, <span class="html-italic">Acinetobacter baumannii</span>, <span class="html-italic">Pseudomonas aeruginosa</span>, and <span class="html-italic">Enterobacter cloacae</span>), negative samples (<span class="html-italic">Candida parapsilosis</span>, <span class="html-italic">Candida tropicalis</span>, <span class="html-italic">Candida glabrata</span>, and <span class="html-italic">Candida albicans</span>), and distilled water used as a no-template control (NTC). (<b>B</b>) Amplification curves generated using the pan-fungal DreamDX primer–probe set for positive samples (<span class="html-italic">C. parapsilosis</span>, <span class="html-italic">C. tropicalis</span>, <span class="html-italic">C. glabrata</span>, and <span class="html-italic">C. albicans</span>), negative samples (<span class="html-italic">S. epidermidis</span>, <span class="html-italic">S. aureus</span>, <span class="html-italic">E. faecalis</span>, <span class="html-italic">S. dysgalactiae</span>, <span class="html-italic">S. pyogenes</span>, <span class="html-italic">E. coli</span>, <span class="html-italic">A. baumannii</span>, <span class="html-italic">P. aeruginosa</span>, and <span class="html-italic">E. cloacae</span>), and distilled water used as an NTC.</p> Full article ">Figure 4
<p>Flow diagram of sample selection based on GAPDH validation, microorganism identification, and pan-bacterial DreamDX qPCR results in PJI and non-PJI groups.</p> Full article ">Figure 5
<p>ROC curve of sensitivity (%) versus specificity (%) of the qPCR assay using the DreamDX primer–hydrolysis probe set for pan-bacterial detection of clinical SFs. The “***” character means that the <span class="html-italic">p</span> values less than 0.001.</p> Full article ">Figure 6
<p>Inflammatory factor levels for CP and CN groups. (<b>A</b>) TP in serum, (<b>B</b>) CRP in serum, (<b>C</b>) erythrocyte sedimentation rate (ESR) in whole blood, (<b>D</b>) white blood cells (WBCs) in whole blood, and (<b>E</b>) white blood cells (WBCs) in synovial fluid. (<b>F</b>) Associations of inflammatory factors.</p> Full article ">
14 pages, 2633 KiB  
Article
Swine Colibacillosis: Analysis of the Gut Bacterial Microbiome
by Wanli Sha, Emad Beshir Ata, Man Yan, Zhijie Zhang and Honggang Fan
Microorganisms 2024, 12(6), 1233; https://doi.org/10.3390/microorganisms12061233 - 19 Jun 2024
Cited by 1 | Viewed by 1290
Abstract
This study aimed to evaluate the disruption of the swine gut microbiota and histopathological changes caused by infection with enterotoxigenic E. coli. Fecal samples were collected from piglets suffering from diarrhea post-recovery and healthy animals. Intestinal tissues were collected for histopathological changes. [...] Read more.
This study aimed to evaluate the disruption of the swine gut microbiota and histopathological changes caused by infection with enterotoxigenic E. coli. Fecal samples were collected from piglets suffering from diarrhea post-recovery and healthy animals. Intestinal tissues were collected for histopathological changes. The results revealed histopathological changes mainly in the ileum of the infected animals compared to those in the ileum of the control and recovered animals. The operational taxonomic units (OTUs) revealed that the E. coli diarrheal group exhibited the highest bacterial richness. Principal coordinate analysis (PCoA) corroborated the presence of dysbiosis in the gut microbiota following E. coli-induced diarrhea. While the normal control and infected groups displayed slight clustering, the recovery group formed a distinct cluster with a distinct flora. Bacteroidetes, Firmicutes, and Fusobacteria were the dominant phyla in both the healthy and recovered piglets and in the diarrheal group. LEfSe and the associated LDA score analysis revealed that the recovered group exhibited dominance of the phyla Euryarchaeota and Bacteroidota, while groups N and I showed dominance of the phyla Firmicutes and Fusobacteriota, respectively. The LDA scores highlighted a significant expression of the Muribaculacea family in group R. The obtained findings will help in understanding the microbiome during swine colibacillosis, which will support control of the outbreaks. Full article
(This article belongs to the Special Issue State-of-the-Art Veterinary Microbiology in China (2023, 2024))
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<p>Histopathological changes in the small intestine segments. Duodenal, jejunal, and ileal samples of piglets infected with <span class="html-italic">E. coli</span> from pig farm were examined in pathological tissue sections and with HE staining (Bar = 100 µm).</p> Full article ">Figure 2
<p>Venn map and alpha-diversity analysis. (<b>A</b>) Venn diagrams of different groups varieties. (<b>B</b>) Observed_species. (<b>C</b>) Chao1 observed number of species. (<b>D</b>) Shannon–Wiener index. (<b>E</b>) ACE. (<b>F</b>) PD_whole_tree. One-way ANOVA was employed for the statistical analysis (** <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). Abbreviations: N, samples from normal control group; I, samples from the infected group; R, samples from recovery group.</p> Full article ">Figure 3
<p>β-diversity and analysis of flora composition. (<b>A</b>) Beta diversity. (<b>B</b>) Analysis of flora composition at phylum level. (<b>C</b>) Analysis of flora composition at class level. (<b>D</b>) Heatmaps. Abbreviations: N, samples from normal control group; I, samples from the infected group; R, samples from recovery group.</p> Full article ">Figure 3 Cont.
<p>β-diversity and analysis of flora composition. (<b>A</b>) Beta diversity. (<b>B</b>) Analysis of flora composition at phylum level. (<b>C</b>) Analysis of flora composition at class level. (<b>D</b>) Heatmaps. Abbreviations: N, samples from normal control group; I, samples from the infected group; R, samples from recovery group.</p> Full article ">Figure 4
<p>LEfSe analysis. (<b>A</b>) Cladogram of the LEfSe analysis of the gut microbiota in different groups. (<b>B</b>) Histogram of the LDA scores computed for features differentially abundant among N, I, and R piglets. LDA scores obtained from the LEfSe analysis of the gut microbiota in different groups. An LDA effect size of greater than 3 was used as a threshold for the LEfSe analysis. Abbreviations: N, samples from normal control group; I, samples from the infected group; R, samples from recovery group.</p> Full article ">Figure 5
<p>Microbial function prediction of three groups of pig’s gut bacteria. The picture indicated the KEGG functional category. (<b>A</b>) shows level 2 of KEGG functional category. (<b>B</b>) shows level 3 of KEGG functional category. Abbreviations: N, samples from normal control group; I, samples from the infected group; R, samples from recovery group.</p> Full article ">
6 pages, 877 KiB  
Opinion
The Enigma of NTH2 Gene in Yeasts
by Sergi Maicas, Ruth Sánchez-Fresneda, Francisco Solano and Juan-Carlos Argüelles
Microorganisms 2024, 12(6), 1232; https://doi.org/10.3390/microorganisms12061232 - 19 Jun 2024
Viewed by 1015
Abstract
The enzymatic hydrolysis of the non-reducing disaccharide trehalose in yeasts is carried out by trehalase, a highly specific α–glucosidase. Two types of such trehalase activity are present in yeasts, and are referred to as neutral and acid enzymes. They are encoded by [...] Read more.
The enzymatic hydrolysis of the non-reducing disaccharide trehalose in yeasts is carried out by trehalase, a highly specific α–glucosidase. Two types of such trehalase activity are present in yeasts, and are referred to as neutral and acid enzymes. They are encoded by distinct genes (NTH1 and ATH1, respectively) and exhibit strong differences in their biochemical and physiological properties as well as different subcellular location and regulatory mechanisms. Whereas a single gene ATH1 codes for acid trehalase, the genome of some yeasts appears to predict the existence of a second redundant neutral trehalase, encoded by the NTH2 gene, a paralog of NTH1. In S. cerevisiae the corresponding two proteins share 77% amino acid identity, leading to the suggestion that NTH2 codes for a functional trehalase activity. However, Nth2p lacks any measurable neutral trehalase activity and disruption of NTH2 gene has no effect on this activity compared to a parental strain. Likewise, single nth1Δ and double nth1Δ/nth2Δ null mutants display no detectable neutral activity. Furthermore, disruption of NTH2 does not cause any apparent phenotype apart from a slight involvement in thermotolerance. To date, no evidence of a duplicated NTH gene has been recorded in other archetypical yeasts, like C. albicans or C. parapsilosis, and a possible regulatory mechanism of Nth2p remains unknown. Therefore, although genomic analysis points to the existence, in some yeasts, of two distinct genes encoding trehalase activities, the large body of biochemical and physiological evidence gathered from NTH2 gene does not support this proposal. Indeed, much more experimental evidence would be necessary to firmly validate this hypothesis. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>Alignment of the amino acid sequences corresponding to Nth1 and Nth2 enzymes in the yeasts <span class="html-italic">Candida glabrata</span> and <span class="html-italic">Saccharomyces cerevisiae</span>. The alignment was performed using the MUSCLE program (Multiple Sequence Comparison by Log-Expectation). The text indicates the residues that are identical (*), conserved substitutions residues (:) and semi-conserved substitutions (.). Boxes indicate the essential residues involved in trehalose binding and catalytic activities.</p> Full article ">
15 pages, 3530 KiB  
Article
Presence and Persistence of ESKAPEE Bacteria before and after Hospital Wastewater Treatment
by Miguel Galarde-López, Maria Elena Velazquez-Meza, Elizabeth Ernestina Godoy-Lozano, Berta Alicia Carrillo-Quiroz, Patricia Cornejo-Juárez, Alejandro Sassoé-González, Alfredo Ponce-de-León, Pedro Saturno-Hernández and Celia Mercedes Alpuche-Aranda
Microorganisms 2024, 12(6), 1231; https://doi.org/10.3390/microorganisms12061231 - 19 Jun 2024
Cited by 1 | Viewed by 1908
Abstract
The metagenomic surveillance of antimicrobial resistance in wastewater has been suggested as a methodological tool to characterize the distribution, status, and trends of antibiotic-resistant bacteria. In this study, a cross-sectional collection of samples of hospital-associated raw and treated wastewater were obtained from February [...] Read more.
The metagenomic surveillance of antimicrobial resistance in wastewater has been suggested as a methodological tool to characterize the distribution, status, and trends of antibiotic-resistant bacteria. In this study, a cross-sectional collection of samples of hospital-associated raw and treated wastewater were obtained from February to March 2020. Shotgun metagenomic sequencing and bioinformatic analysis were performed to characterize bacterial abundance and antimicrobial resistance gene analysis. The main bacterial phyla found in all the samples were as follows: Proteobacteria, Bacteroides, Firmicutes, and Actinobacteria. At the species level, ESKAPEE bacteria such as E. coli relative abundance decreased between raw and treated wastewater, but S. aureus, A. baumannii, and P. aeruginosa increased, as did the persistence of K. pneumoniae in both raw and treated wastewater. A total of 172 different ARGs were detected; blaOXA, blaVEB, blaKPC, blaGES, mphE, mef, erm, msrE, AAC(6′), ant(3″), aadS, lnu, PBP-2, dfrA, vanA-G, tet, and sul were found at the highest abundance and persistence. This study demonstrates the ability of ESKAPEE bacteria to survive tertiary treatment processes of hospital wastewater, as well as the persistence of clinically important antimicrobial resistance genes that are spreading in the environment. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Bacterial community composition in hospital wastewater samples. Taxonomic annotation at the kingdom level.</p> Full article ">Figure 2
<p>Bacterial community composition in hospital wastewater samples. Relative abundance at the phylum level.</p> Full article ">Figure 3
<p>Alpha diversity indexes of each treatment at the species level, Chao1 diversity index, Shannon’s diversity index, and Simpson diversity index.</p> Full article ">Figure 4
<p>Analysis of similarity (ANOSIM). Principal coordinate analysis between treatments using the Bray–Curtis distance matrix at the species level.</p> Full article ">Figure 5
<p>Analysis of the differential abundance of the genera of the <span class="html-italic">ESKAPEE</span> group by wastewater type.</p> Full article ">Figure 6
<p>Relative abundance of <span class="html-italic">ESKAPEE</span> group bacteria at the species level. (<b>A</b>) Relative abundance (log) in each hospital wastewater sample before and after the wastewater treatment plant. (<b>B</b>) Relative abundance of <span class="html-italic">ESKAPEE</span> group bacteria.</p> Full article ">Figure 7
<p>Relative abundance of antibiotic resistance genes by CARD. Green: HRAEI. Blue: INCAN.</p> Full article ">Figure 8
<p>Relative abundance of resistance genes encoding antibiotic classes in wastewater samples. Drug class: Agly (aminoglycoside), Bla (betalactam), Flq (fluoroquinolana), Gly (glycopeptides), MLS (macrolides–lincosamides–streptogramines), Ntmdz (nitroimidazole), Phe (phenicol), Rif (rifampicin), Sul (sulfonamides), Tet (tetracycline), and Tmt (trimethoprim).</p> Full article ">
14 pages, 1555 KiB  
Article
Nisin Inhibition of Gram-Negative Bacteria
by Adam M. Charest, Ethan Reed, Samantha Bozorgzadeh, Lorenzo Hernandez, Natalie V. Getsey, Liam Smith, Anastasia Galperina, Hadley E. Beauregard, Hailey A. Charest, Mathew Mitchell and Margaret A. Riley
Microorganisms 2024, 12(6), 1230; https://doi.org/10.3390/microorganisms12061230 - 19 Jun 2024
Cited by 5 | Viewed by 3194
Abstract
Aims: This study investigates the activity of the broad-spectrum bacteriocin nisin against a large panel of Gram-negative bacterial isolates, including relevant plant, animal, and human pathogens. The aim is to generate supportive evidence towards the use/inclusion of bacteriocin-based therapeutics and open avenues for [...] Read more.
Aims: This study investigates the activity of the broad-spectrum bacteriocin nisin against a large panel of Gram-negative bacterial isolates, including relevant plant, animal, and human pathogens. The aim is to generate supportive evidence towards the use/inclusion of bacteriocin-based therapeutics and open avenues for their continued development. Methods and Results: Nisin inhibitory activity was screened against a panel of 575 strains of Gram-negative bacteria, encompassing 17 genera. Nisin inhibition was observed in 309 out of 575 strains, challenging the prevailing belief that nisin lacks effectiveness against Gram-negative bacteria. The genera Acinetobacter, Helicobacter, Erwinia, and Xanthomonas exhibited particularly high nisin sensitivity. Conclusions: The findings of this study highlight the promising potential of nisin as a therapeutic agent for several key Gram-negative plant, animal, and human pathogens. These results challenge the prevailing notion that nisin is less effective or ineffective against Gram-negative pathogens when compared to Gram-positive pathogens and support future pursuits of nisin as a complementary therapy to existing antibiotics. Significance and Impact of Study: This research supports further exploration of nisin as a promising therapeutic agent for numerous human, animal, and plant health applications, offering a complementary tool for infection control in the face of multidrug-resistant bacteria. Full article
(This article belongs to the Special Issue The Potential of Antimicrobial Activity and Antibiofilm Activity of Bacteriocins)
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<p>Pathways of nisin inhibition against Gram-positive (<b>A</b>) and Gram-negative (<b>B</b>) bacteria.</p> Full article ">Figure 2
<p>The rank order of nisin sensitivity (from least to highest) for 17 genera of Gram-negative bacteria. Isolates of <span class="html-italic">P. aeruginosa</span> and <span class="html-italic">P. syringae</span> are provided separately.</p> Full article ">Figure 3
<p>Summary statistics of MIC data for Gram-negative bacteria: Violin plots of MICs for 17 genera of Gram-negative bacteria are provided. MICs are on the y-axis, and genera are indicated on the x-axis. <span class="html-italic">P. aeruginosa</span> and <span class="html-italic">P. syringae</span> are provided separately. Thin black horizontal lines indicate quartiles; thick black horizontal lines indicate medians; widths of violin plots represent the proportion of isolates for a given MIC, and black dots are where clusters of similar MICs fall.</p> Full article ">
16 pages, 4428 KiB  
Article
Spatial Chromosome Organization and Adaptation of Escherichia coli under Heat Stress
by Xu-Ting Wang and Bin-Guang Ma
Microorganisms 2024, 12(6), 1229; https://doi.org/10.3390/microorganisms12061229 - 19 Jun 2024
Cited by 1 | Viewed by 1437
Abstract
The spatial organization of bacterial chromosomes is crucial for cellular functions. It remains unclear how bacterial chromosomes adapt to high-temperature stress. This study delves into the 3D genome architecture and transcriptomic responses of Escherichia coli under heat-stress conditions to unravel the intricate interplay [...] Read more.
The spatial organization of bacterial chromosomes is crucial for cellular functions. It remains unclear how bacterial chromosomes adapt to high-temperature stress. This study delves into the 3D genome architecture and transcriptomic responses of Escherichia coli under heat-stress conditions to unravel the intricate interplay between the chromosome structure and environmental cues. By examining the role of macrodomains, chromosome interaction domains (CIDs), and nucleoid-associated proteins (NAPs), this work unveils the dynamic changes in chromosome conformation and gene expression patterns induced by high-temperature stress. It was observed that, under heat stress, the short-range interaction frequency of the chromosomes decreased, while the long-range interaction frequency of the Ter macrodomain increased. Furthermore, two metrics, namely, Global Compactness (GC) and Local Compactness (LC), were devised to measure and compare the compactness of the chromosomes based on their 3D structure models. The findings in this work shed light on the molecular mechanisms underlying thermal adaptation and chromosomal organization in bacterial cells, offering valuable insights into the complex inter-relationships between environmental stimuli and genomic responses. Full article
(This article belongs to the Special Issue Molecular Mechanism of Microbial Heat Adaptation)
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<p><b><span class="html-italic">E. coli</span> chromosome interactions under different growth conditions.</b> (<b>A</b>) Interaction frequency of the DNA segments varies with the linear genomic distance. (<b>B</b>) Heat maps for the ratio of the interaction frequency between the high- and normal-temperature growth conditions. Blue indicates a decrease in the interaction frequency under the high-temperature condition, and red indicates an increase. The green dashed lines in the figure indicate 200 kb. (<b>C</b>) Short-range (&lt;100 kb) interaction proportions under different growth conditions. (<b>D</b>) Short-range (&lt;100 kb) interaction frequencies under different growth conditions. The white dashed lines in the subfigures (<b>C</b>,<b>D</b>) indicate 100 kb. The black dotted vertical lines in the subfigures (<b>B</b>–<b>D</b>) indicate the boundaries of the macrodomains.</p> Full article ">Figure 2
<p><b>The relationship between the DNA interaction frequency and transcription level and the CID boundaries under different growth conditions.</b> (<b>A</b>) Correlation between the interaction frequency and transcription level of the DNA segments under different growth conditions. Blue lines represent the Z-score of the DNA interaction frequency, and red lines represent the Z-score of the DNA transcription level. The lower left corner of each subfigure displays the correlation coefficient and corresponding significance level (<span class="html-italic">p</span>-value). (<b>B</b>) CID boundaries under different growth conditions. The horizontal lines represent the genomic coordinates, and the points on each line represent the CID boundaries.</p> Full article ">Figure 3
<p><b>The 3D structural features of the <span class="html-italic">E. coli</span> chromosome under different growth conditions.</b> (<b>A</b>) The 3D structural models of the <span class="html-italic">E. coli</span> chromosome under different growth conditions. In the Norm_Log and Norm_Sta models, green is the Ori macrodomain, red is the Ter macrodomain, purple and blue are the Left and Right macrodomains, respectively, and yellow is the non-structured regions; in the Therm_Log and Therm_Sta models, lighter colors are used correspondingly. (<b>B</b>) Distribution of the distance between points (bins) in these 3D models. (<b>C</b>) Spatial distances between the bins of the macrodomains in the 3D models of the <span class="html-italic">E. coli</span> chromosome under different growth conditions. O: Ori macrodomain; T: Ter macrodomain; L: Left macrodomain; R: Right macrodomain. OT represents the distances between the bins in the Ori macrodomain and the bins in the Ter macrodomain, and so on. (<b>D</b>) Global Compactness of the <span class="html-italic">E. coli</span> chromosome models under different growth conditions.</p> Full article ">Figure 4
<p><b>Comparison of the <span class="html-italic">E. coli</span> nucleoid morphology in different growth conditions.</b> (<b>A</b>) Typical microscopic images of the <span class="html-italic">E. coli</span> nucleoid under various growth conditions. The nucleoids of <span class="html-italic">E. coli</span> were stained blue by DAPI. (<b>B</b>) Statistical results of the <span class="html-italic">E. coli</span> nucleoid width under different growth conditions. (<b>C</b>) Statistical results of the <span class="html-italic">E. coli</span> nucleoid length under different growth conditions. In B and C, significance levels are denoted by the following symbols: ns for <span class="html-italic">p</span> &gt; 0.05 and **** for <span class="html-italic">p</span> ≤ 0.0001.</p> Full article ">Figure 5
<p><b>Local Compactness of the <span class="html-italic">E. coli</span> chromosome and related features.</b> (<b>A</b>) The Local Compactness of the <span class="html-italic">E. coli</span> chromosome 3D structures under different growth conditions and its ratio between high and normal temperatures. The green dashed lines in the bottom two subfigures of figure A indicate the scale of 200 kb. (<b>B</b>) The Local Compactness in the 100 kb range of the <span class="html-italic">E. coli</span> chromosome 3D structures under different growth conditions. (<b>C</b>) The Local Compactness in the 500 kb range of the <span class="html-italic">E. coli</span> chromosome 3D structures under different growth conditions. In subfigures (<b>B</b>,<b>C</b>), the lines of different colors correspond to different growth conditions. The dots on the purple horizontal lines represent the location of MatS, which is the binding site of MatP. The black dashed vertical lines in the subfigures (<b>A</b>–<b>C</b>) represent the boundaries of the macrodomains. (<b>D</b>,<b>E</b>) Transcription levels of MatP and MukBEF in <span class="html-italic">E. coli</span> under different growth conditions, respectively.</p> Full article ">Figure 6
<p><b>Correlation between the Local Compactness and transcription level.</b> (<b>A</b>) The genome-wide correlation between the Local Compactness and transcription level under different growth conditions. (<b>B</b>) The correlation between the transcription levels of four sigma factors and their Local Compactness (where the sigma factor genes reside in the linear genome). Green lines correspond to the Local Compactness; red lines correspond to the transcription level.</p> Full article ">
15 pages, 4713 KiB  
Article
A Genomics-Based Discovery of Secondary Metabolite Biosynthetic Gene Clusters in the Potential Novel Strain Streptomyces sp. 21So2-11 Isolated from Antarctic Soil
by Yu Du, Wei Han, Puyu Hao, Yongqiang Hu, Ting Hu and Yinxin Zeng
Microorganisms 2024, 12(6), 1228; https://doi.org/10.3390/microorganisms12061228 - 19 Jun 2024
Cited by 1 | Viewed by 2129
Abstract
Streptomyces species are attractive sources of secondary metabolites that serve as major sources of antibiotics and other drugs. In this study, genome mining was used to determine the biosynthetic potential of Streptomyces sp. 21So2-11 isolated from Antarctic soil. 16S rRNA gene sequencing revealed [...] Read more.
Streptomyces species are attractive sources of secondary metabolites that serve as major sources of antibiotics and other drugs. In this study, genome mining was used to determine the biosynthetic potential of Streptomyces sp. 21So2-11 isolated from Antarctic soil. 16S rRNA gene sequencing revealed that this strain is most closely related to Streptomyces drozdowiczii NBRC 101007T, with a similarity of 98.02%. Genome comparisons based on average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) showed that strain 21So2-11 represents a novel species of the genus Streptomyces. In addition to a large number of genes related to environmental adaptation and ecological function, a total of 28 putative biosynthetic gene clusters (BGCs) responsible for the biosynthesis of known and/or novel secondary metabolites, including terpenes, lantipeptides, polyketides, nonribosomal peptides, RiPPs and siderophores, were detected in the genome of strain 21So2-11. In addition, a total of 1456 BGCs were predicted to contribute to the biosynthesis of more than 300 secondary metabolites based on the genomes of 47 Streptomyces strains originating from polar regions. The results indicate the potential of Streptomyces sp. 21So2-11 for bioactive secondary metabolite production and are helpful for understanding bacterial adaptability and ecological function in cold terrestrial environments. Full article
(This article belongs to the Section Microbial Biotechnology)
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<p>Maximum likelihood (ML) tree based on 16S rRNA gene sequences showing the phylogenetic position of strain 21So2-11 within the genus <span class="html-italic">Streptomyces</span>. Bootstrap values above 50% based on 1000 replicates are shown at branch nodes. <span class="html-italic">Kitasatospora setae</span> KM-6054<sup>T</sup> was used as an outgroup. The scale bar corresponds to 0.01 substitutions per nucleotide position.</p> Full article ">Figure 2
<p>Whole-genome-sequence-based phylogenetic tree of strain 21So2-11 with closely related type strains. The numbers above the branches represent genome BLAST distance phylogeny (GBDP) pseudobootstrap values greater than 75% based on 100 replicates. The scale bar corresponds to 0.01 substitutions per nucleotide position. <span class="html-italic">Kitasatospora setae</span> KM-6054<sup>T</sup> was used as an outgroup.</p> Full article ">Figure 3
<p>Circular visualization of the genome of <span class="html-italic">Streptomyces</span> sp. 21So2-11. The outer circle represents the distribution of gene clusters coding for secondary metabolites (red: clusters that are &gt;75% similar to those BGCs present in related organisms; kermesinus: &lt;75% similarity). The gene clusters are followed by COG on the forward (the second circle) and reverse (the third circle) strands (colored by COG categories). The fourth and fifth circles represent coding regions (CDSs), tRNAs (red bars) and rRNA operons (blue bars) in the sense and antisense directions, respectively. The order of the scaffolds is represented in the sixth circle. Histograms in the seventh circle indicate the GC content per 10,000 bases. The eighth circle represents GC skew data per 10,000 bases (green indicates positive skewness, and purple indicates negative skewness). The innermost circle represents the number of bases.</p> Full article ">Figure 4
<p>Comparison of the naphthomycin gene cluster in strain 21So2-11 (cluster 9.1 in <a href="#app1-microorganisms-12-01228" class="html-app">Table S3</a>) with those in <span class="html-italic">Streptomyces</span> sp. 11-1-2, <span class="html-italic">S. hygroscopicus</span> XM201 and <span class="html-italic">Actinoplanes teichomyceticus</span> ATCC 31121. Homologous genes among the four bacterial strains are shown in the same colors. Genes without any color in strain 21So2-11 are of unknown function, whereas those in the other species have no homologs in 21So2-11.</p> Full article ">Figure 5
<p>Heatmap of BGC types in the genomes of strain 21So2-11 and 46 other polar <span class="html-italic">Streptomyces</span> strains identified using antiSMASH and BiG-SCAPE.</p> Full article ">Figure 6
<p>Sequence similarity network of 28 BGCs detected in strain 21So2-11 compared against BGCs in 46 other polar <span class="html-italic">Streptomyces</span> strains. Nodes in stars, triangles and circles represent BGCs originating from strain 21So2-11, 41 Antarctic <span class="html-italic">Streptomyces</span> strains, and 5 Arctic <span class="html-italic">Streptomyces</span> strains, respectively. Clusters of nodes associated with a MIBiG BGC are all presented separately. The colors are shown according to different BGC family annotations.</p> Full article ">
11 pages, 2333 KiB  
Article
The Description and Analysis of the Complete Genome of Dermacoccus barathri FBCC-B549 Strain
by Yeha Kim, Hyaekang Kim, Jina Kim, Ji-Hye Han, Eu Jin Chung, Seung Won Nam, Miyoung Shin and Woori Kwak
Microorganisms 2024, 12(6), 1227; https://doi.org/10.3390/microorganisms12061227 - 18 Jun 2024
Viewed by 1507
Abstract
Dermacoccus barathri is the first reported pathogen within the Dermacoccus genus to cause a catheter-related bloodstream infection, which occurred in 2015. In this study, the complete genome assembly of Dermacoccus barathri was constructed, and the complete genome of Dermacoccus barathri FBCC-B549 consists of [...] Read more.
Dermacoccus barathri is the first reported pathogen within the Dermacoccus genus to cause a catheter-related bloodstream infection, which occurred in 2015. In this study, the complete genome assembly of Dermacoccus barathri was constructed, and the complete genome of Dermacoccus barathri FBCC-B549 consists of a single chromosome (3,137,745 bp) without plasmids. The constructed genome of D. barathri was compared with those of two closely related species within the Dermacoccus genus. D. barathri exhibited a pattern similar to Dermacoccus abyssi in terms of gene clusters and synteny analysis. Contrary to previous studies, biosynthetic gene cluster (BGC) analysis for predicting secondary metabolites revealed the presence of the LAP biosynthesis pathway in the complete genome of D. barathri, predicting the potential synthesis of the secondary metabolite plantazolicin. Furthermore, an analysis to investigate the potential pathogenicity of D. barathri did not reveal any antibiotic resistance genes; however, nine virulence factors were identified in the Virulence Factor Database (VFDB). According to these matching results in the VFDB, despite identifying a few factors involved in biofilm formation, further research is required to determine the actual impact of D. barathri on pathogenicity. The complete genome of D. barathri is expected to serve as a valuable resource for future studies on D. barathri, which currently lack sufficient genomic sequence information. Full article
(This article belongs to the Section Microbial Biotechnology)
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<p>A circular genome map of <span class="html-italic">D. barathri</span> generated using Prokka and Proksee, illustrating the locations of coding sequences (CDSs) within the genome.</p> Full article ">Figure 2
<p>(<b>A</b>) COG annotation results for <span class="html-italic">D. barathri</span>, with each letter representing a class of COG categories. (<b>B</b>) RAST annotation results for <span class="html-italic">D. barathri</span>, with the pie chart displaying Subsystem Feature Counts and the number of genes associated with specific functions.</p> Full article ">Figure 3
<p>Locally collinear block (LCB) comparison among complete genomes of <span class="html-italic">D. barathri</span>, <span class="html-italic">D. abyssi</span> (<b>A</b>), and <span class="html-italic">D. nishinomiyaensis</span> (<b>B</b>). Colored blocks indicate aligned genome sequences, highlighting homologous and conserved regions.</p> Full article ">Figure 4
<p>Orthologous gene cluster analysis using OrthoVenn3, depicting shared orthologous clusters among three <span class="html-italic">Dermacoccus</span> species. Numbers adjacent to species indicate the total clusters in each list.</p> Full article ">
13 pages, 2627 KiB  
Article
The Utilization of Bacillus subtilis to Design Environmentally Friendly Living Paints with Anti-Mold Properties
by Yuval Dorfan, Avichay Nahami, Yael Morris, Benny Shohat and Ilana Kolodkin-Gal
Microorganisms 2024, 12(6), 1226; https://doi.org/10.3390/microorganisms12061226 - 18 Jun 2024
Viewed by 1786
Abstract
The anti-fungal properties of the probiotic bacterium Bacillus subtilis have been studied extensively in agriculture and ecology, but their applications in the built environment remain to be determined. Our work aims to utilize this biological component to introduce new diverse anti-mold properties into [...] Read more.
The anti-fungal properties of the probiotic bacterium Bacillus subtilis have been studied extensively in agriculture and ecology, but their applications in the built environment remain to be determined. Our work aims to utilize this biological component to introduce new diverse anti-mold properties into paint. “Mold” refers to the ubiquitous fungal species that generate visible multicellular filaments commonly found in household dust. The development of mold leads to severe health problems for occupants, including allergic response, hypersensitivity pneumonitis, and asthma, which have significant economic and clinical outcomes. We here demonstrate the robust effect of a commercial paint enhanced with Bacillus subtilis cells against the common mold agent, Aspergillus niger, and identify three biosynthetic clusters essential for this effect. Our results lay the foundation for bio-convergence and synthetic biology approaches to introduce renewable and environmentally friendly bio-anti-fungal agents into the built environment. Full article
(This article belongs to the Special Issue An Update on Bacillus)
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<p><b>Anti-mold activities of <span class="html-italic">B. subtilis</span>.</b> (<b>A</b>) The growth of <span class="html-italic">A. niger</span> colony (white) is limited when grown either in isolation (left) or next to <span class="html-italic">B. subtilis</span> (brown) (right) on LBGM medium. Scale bar: 2 mm. (<b>B</b>) The conditioned media were collected from <span class="html-italic">B. subtilis</span> grown overnight in the indicated media. The collected growth media were concentrated (x50) on C-18 sep-pack and extracted with methanol. The extract was diluted to 1:50 in RPMI medium after 18 h, as carried out previously [<a href="#B50-microorganisms-12-01226" class="html-bibr">50</a>]. For more information, see Materials and Methods. The graphs represent mean and standard deviation of six independent repeats. (<b>C</b>) The conditioned media from <span class="html-italic">B. subtilis</span> grown overnight in indicated media were collected, and concentrated (x50) on C-18 sep-pack column prior to its extraction with methanol. The <span class="html-italic">A. niger</span> cells were diluted in either the conditioned media or with conditioned medium diluted in a RPMI medium to the indicated concentrations (CM dilution). Cultures were plated after 18 h, as carried out previously [<a href="#B50-microorganisms-12-01226" class="html-bibr">50</a>]. For more information, see Materials and Methods. The graphs represent the results obtained with 3 independent repeats carried out in duplicate. Grey [<a href="#B44-microorganisms-12-01226" class="html-bibr">44</a>,<a href="#B47-microorganisms-12-01226" class="html-bibr">47</a>] indicates no colonies were observed. [+] Colonies were observed at 10<sup>−1</sup> dilution.</p> Full article ">Figure 2
<p><b>Anti-mold activities of <span class="html-italic">B. subtilis</span> are due to the synergistic activation of anti-fungal biosynthetic clusters.</b> The conditioned media from <span class="html-italic">B. subtilis</span> and its mutant derivatives grown overnight in LBGM (<b>A</b>) or indicated growth media (<b>B</b>) were collected and tested for the inhibition of <span class="html-italic">A. niger</span> as described in <a href="#microorganisms-12-01226-f001" class="html-fig">Figure 1</a>. The graphs represent the mean and standard deviation of six independent repeats.</p> Full article ">Figure 3
<p><b>Anti-mold activities of <span class="html-italic">B. subtilis</span> are maintained in paint.</b> (<b>A</b>) Whatman disk was either unsoaked (empty disk) or soaked in commercial paint diluted with water 1:1 (paint) or soaked in commercial paint diluted with <span class="html-italic">B. subtilis</span> overnight culture (paint + B.s) as indicated. (<b>B</b>) Image analysis of the fungal growth.</p> Full article ">Figure 4
<p><b>Quantification of anti-fungal activities of <span class="html-italic">B. subtilis</span>-based paint.</b> Commercial anti-mold or commercial paint diluted with <span class="html-italic">B. subtilis</span> was quantified as described in the supporting information (<a href="#app1-microorganisms-12-01226" class="html-app">Figure S3</a>). Experiments were carried out with six independent repeats. Results are of independent technical repeats for <a href="#microorganisms-12-01226-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p><b>Viability and Sporulation of <span class="html-italic">B. subtilis</span> in paint.</b> The paint–bacteria solution was prepared at indicated times, as described in <a href="#microorganisms-12-01226-f004" class="html-fig">Figure 4</a>. After intense vortexing, 1 mL of the solution was taken. A total of 500 µL was mildly sonicated and evaluated for overall CFU numbers/mL in the solution using serial dilutions and plating (<b>A</b>). The remaining solution was heat-activated (80 °C, 30 min) to eliminate non-sporulating cells before plating versus the untreated half. The percentage of spore cells was calculated (<b>B</b>) by calculating the ratio between spores and untreated cell counts. The graphs represent the mean and standard deviation of five independent repeats. ** represents &lt;0.01, *** &lt;0.001.</p> Full article ">
15 pages, 2185 KiB  
Article
Periodontal Inflammation and Dysbiosis Relate to Microbial Changes in the Gut
by Angela R. Kamer, Smruti Pushalkar, Babak Hamidi, Malvin N. Janal, Vera Tang, Kumar Raghava Chowdary Annam, Leena Palomo, Deepthi Gulivindala, Lidia Glodzik and Deepak Saxena
Microorganisms 2024, 12(6), 1225; https://doi.org/10.3390/microorganisms12061225 - 18 Jun 2024
Cited by 2 | Viewed by 1980
Abstract
Periodontal disease (PerioD) is a chronic inflammatory disease of dysbiotic etiology. Animal models and few human data showed a relationship between oral bacteria and gut dysbiosis. However, the effect of periodontal inflammation and subgingival dysbiosis on the gut is unknown. We hypothesized that [...] Read more.
Periodontal disease (PerioD) is a chronic inflammatory disease of dysbiotic etiology. Animal models and few human data showed a relationship between oral bacteria and gut dysbiosis. However, the effect of periodontal inflammation and subgingival dysbiosis on the gut is unknown. We hypothesized that periodontal inflammation and its associated subgingival dysbiosis contribute to gut dysbiosis even in subjects free of known gut disorders. We evaluated and compared elderly subjects with Low and High periodontal inflammation (assessed by Periodontal Inflamed Surface Area (PISA)) for stool and subgingival derived bacteria (assayed by 16S rRNA sequencing). The associations between PISA/subgingival dysbiosis and gut dysbiosis and bacteria known to produce short-chain fatty acid (SCFA) were assessed. LEfSe analysis showed that, in Low PISA, species belonging to Lactobacillus, Roseburia, and Ruminococcus taxa and Lactobacillus zeae were enriched, while species belonging to Coprococcus, Clostridiales, and Atopobium were enriched in High PISA. Regression analyses showed that PISA associated with indicators of dysbiosis in the gut mainly reduced abundance of SCFA producing bacteria (Radj = −0.38, p = 0.03). Subgingival bacterial dysbiosis also associated with reduced levels of gut SCFA producing bacteria (Radj = −0.58, p = 0.002). These results suggest that periodontal inflammation and subgingival microbiota contribute to gut bacterial changes. Full article
(This article belongs to the Special Issue Intestine as a Hub for Host-Microbe Interactions and Its Impact on Local and Systemic Health)
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<p>Differences in gut bacterial composition between pathogenic (high PISA-p) and nonpathogenic (low PISA-n). Using LEfSe, we determined the most abundant gut bacteria in the 12 High PISA (p) and the 24 Low PISA (n) groups at species level. Of importance, gut bacteria enriched in Low PISA are known as gut beneficial bacteria, while the bacteria associated with the High PISA are linked to gut pathology.</p> Full article ">Figure 2
<p>Gut dysbiotic index correlates with Firmicutes-to-Bacteroides ratio. Although there is no consensus definition for gut dysbiosis [<a href="#B21-microorganisms-12-01225" class="html-bibr">21</a>], Firmicutes/Bacteroidetes (F/B) ratio is accepted as an important index of gut dysbiosis [<a href="#B33-microorganisms-12-01225" class="html-bibr">33</a>]. We showed that Gut-DI defined in our study correlated with Firmicutes-to-Bacteroides ratio (R = 0.36, <span class="html-italic">p</span> = 0.04).</p> Full article ">Figure 3
<p>Gut-DI inversely associated with gut SCFA bacterial index. In regression analyses, the gut SCFA bacterial index inversely correlated with Gut-DI (Radj = −0.43, <span class="html-italic">p</span> = 0.01).</p> Full article ">Figure 4
<p>PISA inversely associated with gut SCFA bacterial index. In regression analyses, PISA score inversely associated with gut SCFA bacterial index (Radj = −0.38, <span class="html-italic">p</span> = 0.03).</p> Full article ">Figure 5
<p>Subgingival-DI inversely associated with gut SCFA bacterial index. In regression analyses, subgingival-DI inversely correlated with gut SCFA bacterial index (Radj = −0.58, <span class="html-italic">p</span> = 0.002).</p> Full article ">Figure 6
<p>Model of hypothetical pathways from periodontal inflammation and dysbiosis to gut bacterial changes. Periodontal bacteria (subgingival pathogenic bacteria) contribute to reductions in the SCFA-producing bacteria and, therefore, the amount of SCFA. Reduced SCFA are known to contribute to gut dysbiosis, with negative consequences on the gut. Periodontal inflammation independently or interactively regulates gut SCFA in addition to contributing to gut dysbiosis. Gut dysbiosis further contributes to other systemic diseases. (blue arrows = negative regulators; red arrows = positive regulators.</p> Full article ">
8 pages, 600 KiB  
Communication
Impact of COVID-19 Restrictions on Incidence of Enteropathogenic Bacteria, Virus, and Parasites in Denmark: A National, Register-Based Study
by Kumanan Rune Nanthan, Eva Plantener, John Coia, Jørgen Engberg, Leif Percival Andersen, Ea Marmolin, Gitte Nyvang Hartmeyer, Hans Linde Nielsen, Christen Rune Stensvold, Anne Line Engsbro, Bente Olesen, Lars Lemming and Ming Chen
Microorganisms 2024, 12(6), 1224; https://doi.org/10.3390/microorganisms12061224 - 18 Jun 2024
Cited by 2 | Viewed by 1233
Abstract
Diarrheal diseases caused by enteric pathogens are a significant public health concern. It is widely considered that close contact between persons, poor hygiene, and consumption of contaminated food are the primary causes of gastroenteritis. Clinical microbiology laboratory observations indicate that the incidence of [...] Read more.
Diarrheal diseases caused by enteric pathogens are a significant public health concern. It is widely considered that close contact between persons, poor hygiene, and consumption of contaminated food are the primary causes of gastroenteritis. Clinical microbiology laboratory observations indicate that the incidence of enteropathogenic microorganisms may have been reduced in Denmark during the COVID-19 pandemic. All Departments of Clinical Microbiology in Denmark provided data on the monthly incidence of Salmonella spp., Escherichia coli, Campylobacter spp., Clostridioides difficile, Norovirus GI+GII, Giardia duodenalis, and Cryptosporidium from March 2018 to February 2021. The data were divided into three periods as follows: Control Period 1 (March 2018 to February 2019); Control Period 2 (March 2019 to February 2020); and the Restriction (pandemic) Period (March 2020 to February 2021). The incidences of pathogenic Salmonella spp.-, Escherichia coli-, and Campylobacter spp.-positive samples decreased by 57.3%, 48.1%, and 32.9%, respectively, during the restriction period. No decrease in C. difficile was observed. Norovirus GI+GII-positive samples decreased by 85.6%. Giardia duodenalis-positive samples decreased by 66.2%. Cryptosporidium species decreased by 59.6%. This study demonstrates a clear decrease in the incidence of enteropathogenic bacteria (except for C. difficile), viruses, and parasites during the SARS-CoV-2 restriction period in Denmark. Full article
(This article belongs to the Section Public Health Microbiology)
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<p>Comparison of positive diarrheagenic <span class="html-italic">E. coli</span>, <span class="html-italic">Campylobacter</span> spp., <span class="html-italic">Salmonella</span> spp., <span class="html-italic">C. difficile</span>, Norovirus, <span class="html-italic">G. duodenalis</span> and <span class="html-italic">Cryptosporidium</span> in Denmark before and during the SARS-CoV-2 Restriction Period. * <span class="html-italic">p</span>-values comparing Control Period 1 vs. the Restriction Period. # <span class="html-italic">p</span>-values comparing Control Period 2 vs. the Restriction Period. ** Indicates statistical difference.</p> Full article ">
17 pages, 3726 KiB  
Article
Development of Multiplex RT qPCR Assays for Simultaneous Detection and Quantification of Faecal Indicator Bacteria in Bathing Recreational Waters
by Marina Carrasco-Acosta and Pilar Garcia-Jimenez
Microorganisms 2024, 12(6), 1223; https://doi.org/10.3390/microorganisms12061223 - 18 Jun 2024
Cited by 1 | Viewed by 1460
Abstract
In this study, we designed and validated in silico and experimentally a rapid, sensitive, and specific multiplex RT qPCR for the detection and quantification of faecal indicator bacteria (FIB) used as microbiological references in marine bathing water regulations (Escherichia coli and intestinal [...] Read more.
In this study, we designed and validated in silico and experimentally a rapid, sensitive, and specific multiplex RT qPCR for the detection and quantification of faecal indicator bacteria (FIB) used as microbiological references in marine bathing water regulations (Escherichia coli and intestinal enterococci). The 16S rRNA gene was used to quantify group-specific enterococci and Escherichia/Shigella and species-specific such as Enterococcus faecalis and E. faecium. Additionally, a ybbW gene encoding allantoin transporter protein was used to detect E. coli. An assessment of marine coastal systems (i.e., marine water and sediment) revealed that intestinal enterococci were the predominant group compared to Escherichia/Shigella. The low contribution of E. faecalis to the intestinal enterococci group was reported. As E. faecalis and E. faecium were reported at low concentrations, it is assumed that other enterococci of faecal origin are contributing to the high gene copy number of this group-specific enterococci. Moreover, low 16S rRNA gene copy numbers with respect to E. faecalis and E. faecium were reported in seawater compared to marine sediment. We conclude that marine sediments can affect the quantification of FIBs included in bathing water regulations. Valuing the quality of the marine coastal system through sediment monitoring is recommended. Full article
(This article belongs to the Collection Advances in Public Health Microbiology)
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<p>Location of the sampling sites in the bay of <span class="html-italic">El Confital</span> (Gran Canaria, Spain): (<b>a</b>) the Canary Archipelago, (<b>b</b>) the island of Gran Canaria, (<b>c</b>) the peninsula of <span class="html-italic">La Isleta</span>, and (<b>d</b>) the bay of <span class="html-italic">El Confital</span>. Sampling site 1 (S1); sampling site 2 (S2); sampling site 3 (S3) (satellite images obtained from Visor IDECanarias, <a href="http://visor.grafcan.es/" target="_blank">http://visor.grafcan.es/</a>, accessed on 4 February 2024).</p> Full article ">Figure 2
<p><span class="html-italic">Enterococcus</span> species annotated in the List of Prokaryotic Names with Standing in Nomenclature (LPSN) [<a href="#B40-microorganisms-12-01223" class="html-bibr">40</a>] and in silico amplified with the group-specific primers and probes designed in this work using UGENE v33.0 software [<a href="#B45-microorganisms-12-01223" class="html-bibr">45</a>]. (<b>a</b>) Species assignation according to the origin and clinical interest of <span class="html-italic">Enterococcus</span> species; (<b>b</b>) Venn diagram of <span class="html-italic">Enterococcus</span> species retrieved from LPSN. HFO: human faecal origin; AFO: animal faecal origin; ANFO: animal non-faecal origin; P/O: pathogenic or opportunistic species; NP: non-pathogenic species; +: shows the positive assignment of the target.</p> Full article ">Figure 3
<p>Correlation between the log<sub>10</sub> gene copy number and quantification cycle (C<sub>q</sub>) values with indications of efficiency and the limit of detection of (<b>a</b>) enterococci, (<b>b</b>) <span class="html-italic">Escherichia</span>/<span class="html-italic">Shigella</span>, (<b>c</b>) <span class="html-italic">E. faecalis</span>, (<b>d</b>) <span class="html-italic">E. faecium</span>, and (<b>e</b>) <span class="html-italic">E. coli</span>. RT qPCR efficiency was calculated according to [<a href="#B53-microorganisms-12-01223" class="html-bibr">53</a>] (<span class="html-italic">n</span> = 3).</p> Full article ">
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