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15 pages, 3659 KiB  
Article
Mr-lac3 and Mr-lcc2 in Metarhizium robertsii Regulate Conidiation and Maturation, Enhancing Tolerance to Abiotic Stresses and Pathogenicity
by Qiaoyun Wu, Yingying Ye, Yiran Liu, Yufan He, Xing Li, Siqi Yang, Tongtong Xu, Xiufang Hu and Guohong Zeng
J. Fungi 2025, 11(3), 176; https://doi.org/10.3390/jof11030176 (registering DOI) - 22 Feb 2025
Abstract
As a type of multicopper oxidase, laccases play multiple biological roles in entomopathogenic fungi, enhancing their survival, development, and pathogenicity. However, the mechanisms by which laccases operate in these fungi remain under-researched. In this study, we identified two laccase-encoding genes, Mr-lac3 and Mr-lcc2 [...] Read more.
As a type of multicopper oxidase, laccases play multiple biological roles in entomopathogenic fungi, enhancing their survival, development, and pathogenicity. However, the mechanisms by which laccases operate in these fungi remain under-researched. In this study, we identified two laccase-encoding genes, Mr-lac3 and Mr-lcc2, from Metarhizium robertsii, both of which are highly expressed during conidiation. Knocking out Mr-lac3 and Mr-lcc2 resulted in a significant increase in the conidial yields of M. robertsii. Furthermore, the relative expression levels of upstream regulators associated with the conidiation pathway were markedly up-regulated in ΔMr-lac3 and ΔMr-lcc2 compared to the wild-type strain during conidiation, indicating that Mr-lac3 and Mr-lcc2 negatively regulate conidia formation. qRT-PCR analyses revealed that Mr-lac3 and Mr-lcc2 are regulated by the pigment synthesis gene cluster, including Mr-Pks1, Mr-EthD, and Mlac1, and they also provide feedback regulation to jointly control pigment synthesis. Additionally, ΔMr-lac3 and ΔMr-lcc2 significantly reduced the trehalose content in conidia and increased the sensitivity to cell wall-perturbing agents, such as Congo red and guaiacol, which led to a marked decrease in tolerance to abiotic stresses. In conclusion, the laccases Mr-lac3 and Mr-lcc2 negatively regulate conidia formation while positively regulating conidial maturation, thereby enhancing tolerance to abiotic stresses and pathogenicity. Full article
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Figure 1
<p>Conidiophores and conidial yields of the disruption, overexpression, and complementation strains of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span>. (<b>A</b>). Top panel of the upper picture: Conidiophores were observed at 3 days post inoculation by spraying 100 µL of a conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) on the PDA plate. Bottom panel: Colony morphology of the WT, disruption, overexpression, and complementation strains. Colony pictures were taken at 15 days post inoculation by applying 5 µL of a conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) onto the center of a PDA plate (diameter 9 cm). Bar, 1 cm. (<b>B</b>). Conidial yields of the WT, disruption, overexpression, and complementation strains. Conidial yields were measured at 15 days post inoculation by spraying 100 µL of a conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) onto the PDA plate. Values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). Conidial yields were repeated three times with three PDA plates (diameter 9 cm) per repeat.</p>
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<p>Expression patterns of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> in <span class="html-italic">M. robertsii</span>. (<b>A</b>). qRT-PCR analysis of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> expression patterns during mycelia grown in nutrient-rich SDY (Sabouraud dextrose broth plus 1% Yeast Extract), conidiation (5 days post inoculation by spraying 100 µL of 1 × 10<sup>7</sup> conidia mL<sup>−1</sup> conidial suspension onto the PDA plate), and cuticle penetration (appressoria-forming germlings on <span class="html-italic">Galleria mellonella</span> cuticle) in the wild-type strain (WT). The expression level of a gene during conidiation and cuticle penetration was calculated relative to that in mycelia grown in SDY, which was set to 1. Values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). (<b>B</b>). A time course analysis of the expression of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> during conidiation in the WT strain. RNA was extracted at the 5 conidiation stages, 2, 3, 5, 8, and 16 days post inoculation, by spraying 100 µL of a conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) onto the PDA plate. The expression level at day 2 was set to 1. qRT-PCR analyses were repeated three times.</p>
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<p>Mr-lac3 and Mr-lcc2 negatively regulated conidiation. RNA was extracted at 5 days post inoculation. Values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). For each gene, the expression level in the WT strain was set to 1. qRT-PCR analyses were repeated three times with three replicates per repeat.</p>
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<p><span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> are involved in conidial pigment biosynthesis. (<b>A</b>). The expression levels of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> were compared between wild-type (WT) and knock-out mutants of the <span class="html-italic">Pks1</span> gene cluster. Values with different letters were found to be significantly different (<span class="html-italic">p</span> &lt; 0.05). (<b>B</b>). The expression levels of <span class="html-italic">Mr-Pks1</span>, <span class="html-italic">Mr-EthD</span>, and <span class="html-italic">Mlac1</span> in the WT strain, <span class="html-italic">ΔMr-lac3</span>, and <span class="html-italic">ΔMr-lcc2</span>. RNA was extracted at 7 days post inoculation. Values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). The expression level of each gene was set to 1 in the WT strain. For each gene, the expression level in the WT strain was set to 1. qRT-PCR analyses were repeated three times with three replicates per repeat.</p>
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<p><span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> are involved in cell wall integrity. (<b>A</b>). The growth rate of colonies of knock-out (KO) mutants, overexpressed strains, and complementary strains of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> under abiotic stress treatments with Congo red (1.5 mg/mL). A 5 μL conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) was inoculated in the center of the PDA containing Congo red (1.5 mg/mL) and cultured at 26 °C for a period of time. The diameter of the colonies was measured every day starting from the third day after inoculation. Pictures of the colonies were taken at 10 days post inoculation. (<b>B</b>). The growth rate of colonies of knock-out (KO) mutants, overexpressed strains, and complementary strains of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> under abiotic stress treatments with guaiacol (0.04%). A 5 μL conidial suspension (1 × 10<sup>7</sup> conidia mL<sup>−1</sup>) was inoculated in the center of the PDA with guaiacol (0.04%) and cultured at 26 °C for a period of time. The diameter of the colonies was measured every day starting from 3 days after inoculation. Colony pictures were taken at 10 days post inoculation. Note: * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>). The expression levels of components involved in cell integrity in <span class="html-italic">ΔMr-lac3</span> and <span class="html-italic">ΔMr-lcc2</span>. RNA was extracted at 5 days post inoculation. Values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). For each gene, the expression level in WT was set to 1. qRT-PCR analyses were repeated three times with three replicates per repeat.</p>
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<p><span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> are involved in conidial trehalose synthesis. The 15-day-old conidia (2 × 10<sup>8</sup> conidia) were washed with ddH<sub>2</sub>O three times, resuspended in 200 µL ddH<sub>2</sub>O, and incubated at 100 °C for 20 min. The suspension was then centrifuged for 10 min at 11,000× <span class="html-italic">g</span>, and the supernatant containing trehalose was collected. The amount of glucose liberated by the activity of trehalose was assayed using a glucose (GO) assay kit and converted into trehalose per conidium (measured in triplicate). Each sample without trehalose treatment served as a negative control. Note: There are significant differences between different letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>GT<sub>50</sub> (time taken for 50% of conidia to germinate) values of knock-out (KO) mutants, overexpressed strains, and complementary strains of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span> under optimal conditions (<b>A</b>) and three abiotic stress treatments [0.005% H<sub>2</sub>O<sub>2</sub> (<b>B</b>), 0.75 M KCl (<b>C</b>), 37 °C (<b>D</b>)]. A 60 μL conidial suspension (4 × 10<sup>7</sup> conidia mL<sup>−1</sup>) was inoculated into 3 mL of 1/2 SDY liquid medium and 1/2 SDY liquid medium with 0.005% H<sub>2</sub>O<sub>2</sub> and 0.75 M KCL added in a Petri dish with a diameter of 3 cm. The culture was incubated at 37 °C and 26 °C for a specific period of time. Conidial germination was observed and counted under an inverted microscope every 2 h. Within the same abiotic stress treatment, values with different letters were significantly different (<span class="html-italic">p</span> &lt; 0.05). The assays were repeated three times with three Petri dishes per repeat.</p>
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<p>Pathogenicity of knock-out (KO) mutants, overexpressed strains, and complementary strains of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span>. (<b>A</b>). The percentage of appressoria-forming germlings on a hydrophobic plastic surface. At each time point, values with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05). Appressorium formation assays were repeated three times with three hydrophobic Petri dishes per repeat. Values with the same letter are not significantly different (<span class="html-italic">p</span> &gt; 0.05). (<b>B</b>). LT<sub>50</sub>: time needed for 50% of a lethal dose. Pathogenicity assays were repeated three times with 40 insects of <span class="html-italic">Galleria mellonella</span> per repeat.</p>
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<p>A schematic model of illustrating the function and regulation of <span class="html-italic">Mr-lac3</span> and <span class="html-italic">Mr-lcc2</span>.</p>
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15 pages, 4171 KiB  
Article
Integrated Microbiology and Metabolomics Analysis Reveal How Tolerant Soybean Cultivar Adapt to Continuous Cropping
by Xingdong Yao, Dexin He, Xiang Zhao, Zhuorui Tan, Hongtao Zhao, Futi Xie and Jingkuan Wang
Agronomy 2025, 15(2), 468; https://doi.org/10.3390/agronomy15020468 - 14 Feb 2025
Abstract
Soybean continuous cropping could alter soil microbial communities, leading to the development of continuous-cropping obstacles that negatively impacted yield. Different soybean cultivars exhibited varying degrees of resistance to these obstacles. However, the mechanisms underlying this resistance remain unclear. In this study, microbiology and [...] Read more.
Soybean continuous cropping could alter soil microbial communities, leading to the development of continuous-cropping obstacles that negatively impacted yield. Different soybean cultivars exhibited varying degrees of resistance to these obstacles. However, the mechanisms underlying this resistance remain unclear. In this study, microbiology and metabolomics were employed to explore the impacts of continuous cropping on rhizosphere microbial communities and metabolite profiles of two soybean cultivars. The results indicated that the cultivars did not reshape the bacterial and fungal community diversity but reshaped their community structures. The potentially pathogenic fungi of continuous-cropping-sensitive soybean cultivar (ACR) were higher than those of continuous-cropping-tolerant soybean cultivar (LCR), which suggested that disease resistance might be a crucial factor in mitigating continuous-cropping barriers. The metabolomic results showed that the rhizosphere soil metabolic profiles of the two soybean cultivars were significantly different, and some rhizosphere soil metabolites, which could promote the growth of pathogens, were higher in ACR than those in LCR. Correlation analysis showed that the differential microbes were closely related to the differential metabolites. All these results suggested that the rhizosphere metabolites of continuous-cropping-sensitive soybean cultivars could promote the growth of pathogens, alter rhizosphere microbial community structure, and subsequently lead to it being more sensitive to soybean continuous-cropping obstacles. Full article
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<p>PCoA of soil bacterial (<b>A</b>) and fungal (<b>B</b>) community structure from the two soybean cultivars at the OTU level, based on Bray–Curtis dissimilarities. Each same color point represents the same soybean cultivar sample, and the distance between points signifies the degree of dissimilarity. LCR, Liaodou14 continuous cropping; ACR, Amsoy continuous cropping.</p>
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<p>The linear discriminant analysis effect size (LEfSe) results for the two soybean cultivars. (<b>A</b>) Cladogram of bacterial taxa of the two soybean cultivars; (<b>B</b>) Cladogram of fungal taxa of the two soybean cultivars; the value of LDA filtrate score was 3.0. The circles emanating from the interior to the exterior signify the taxonomic levels ranging from phylum to genus.</p>
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<p>The linear discriminant analysis effect size (LEfSe) results for the two soybean cultivars. (<b>A</b>) Cladogram of bacterial taxa of the two soybean cultivars; (<b>B</b>) Cladogram of fungal taxa of the two soybean cultivars; the value of LDA filtrate score was 3.0. The circles emanating from the interior to the exterior signify the taxonomic levels ranging from phylum to genus.</p>
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<p>The bacterial (<b>A</b>) and fungal (<b>B</b>) community assembly of the two cultivars. n.s. indicates not significant.</p>
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<p>(<b>A</b>) Classification pie diagram of the identified metabolites at the superclass level. (<b>B</b>) Principal component analysis (PCA) of soil metabolites of different soybean cultivars; different color points represent different soybean cultivars. (<b>C</b>) Volcano plot of differential metabolites between different soybean cultivars. Red represents up metabolites, and green represents down metabolites. (<b>D</b>) Cluster heatmap of differential metabolites in KEGG metabolic pathways containing at least five different metabolites between the two soybean cultivars. Different colors represent the relative content of the metabolite (red indicates high content, green indicates low content).</p>
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<p>(<b>A</b>) The correlation heatmap between bacteria genera and differential metabolites. (<b>B</b>) The correlation heatmap between fungal genera and differential metabolites. The red color signifies the positive correlations, and the blue color signifies the negative correlations. * indicates <span class="html-italic">p</span> &lt; 0.5, ** indicates <span class="html-italic">p</span> &lt; 0.05, and *** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>(<b>A</b>) The correlation heatmap between bacteria genera and differential metabolites. (<b>B</b>) The correlation heatmap between fungal genera and differential metabolites. The red color signifies the positive correlations, and the blue color signifies the negative correlations. * indicates <span class="html-italic">p</span> &lt; 0.5, ** indicates <span class="html-italic">p</span> &lt; 0.05, and *** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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25 pages, 4335 KiB  
Article
Exploring Epigenetic Modifiers in Cowpea: Genomic and Transcriptomic Insights into Histone Methyltransferases and Histone Demethylases
by Jéssica Barbara Vieira Viana, José Ribamar Costa Ferreira-Neto, Eliseu Binneck, Roberta Lane de Oliveira Silva, Antônio Félix da Costa and Ana Maria Benko-Iseppon
Stresses 2025, 5(1), 13; https://doi.org/10.3390/stresses5010013 - 13 Feb 2025
Abstract
Histone methyltransferases (SDGs) and demethylases (JMJs) are well-established regulators of transcriptional responses in plants under adverse conditions. This study characterized SDG and JMJ enzymes in the cowpea (Vigna unguiculata) genome and analyzed their expression patterns under various stress conditions, including root [...] Read more.
Histone methyltransferases (SDGs) and demethylases (JMJs) are well-established regulators of transcriptional responses in plants under adverse conditions. This study characterized SDG and JMJ enzymes in the cowpea (Vigna unguiculata) genome and analyzed their expression patterns under various stress conditions, including root dehydration and mechanical injury followed by CABMV or CPSMV inoculation. A total of 47 VuSDG genes were identified in the cowpea genome and classified into seven distinct classes: I, II, III, IV, V, VI, and VII. Additionally, 26 VuJMJ-coding genes were identified and categorized into their respective groups: Jmj-only, JMJD6, KDM3, KDM5, and KDM4. Analysis of gene expansion mechanisms for the studied loci revealed a predominance of dispersed duplication and WGD/segmental duplication events, with Ka/Ks ratios indicating that all WGD/segmental duplications are under purifying selection. Furthermore, a high degree of conservation was observed for these loci across species, with legumes displaying the highest conservation rates. Cis-Regulatory Element analysis of VuSDG and VuJMJ gene promoters revealed associations with Dof-type and bZIP transcription factors, both of which are known to play roles in plant stress responses and developmental processes. Differential expression patterns were observed for VuSDG and VuJMJ genes under the studied stress conditions, with the highest number of upregulated transcripts detected during the root dehydration assay. Our expression data suggest that as the referred stress persists, the tolerant cowpea accession decreases methylation activity on target histones and shifts towards enhanced demethylation. This dynamic balance between histone methylation and demethylation may regulate the expression of genes linked to dehydration tolerance. During the mechanical injury and viral inoculation assays, VuSDG and VuJMJ transcripts were upregulated exclusively within 60 min after the initial mechanical injury combined with CABMV or CPSMV inoculation, indicating an early role for these enzymes in the plant’s defense response to pathogen exposure. The current study presents a detailed analysis of histone modifiers in cowpea and indicates their role as important epigenetic regulators modulating stress tolerance. Full article
(This article belongs to the Collection Feature Papers in Plant and Photoautotrophic Stresses)
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<p>Neighbor-Joining trees based on the protein sequences of SDGs (<b>A</b>) and JMJs (<b>B</b>) from <span class="html-italic">Arabidopsis thaliana</span> (AtSDGs/AtJMJs, marked with blue circles), rice (OsSDGs/OsJMJs, marked with green circles), and cowpea (VuSDGs/VuJMJs, marked with red circles). Each protein class is represented by branches of distinct colors for easy visualization. In panel (<b>A</b>), different SDG classes are labeled with Roman numerals, while in panel (<b>B</b>), JMJ classes are identified with specific alphanumeric labels. Bootstrap values, derived from 1000 replicates, are shown at each node, indicating the statistical confidence of the phenetic relationships depicted.</p>
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<p>Chromosomal distribution of VuSDG and VuJMJ loci in the cowpea genome. Red and blue marks and labels indicate the positions of VuSDG and VuJMJ genes, respectively, along the 11 studied chromosomes. Dark red ellipses represent centromeres. Chromosome lengths are scaled to the left sidebar, denoted in megabases (Mb).</p>
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<p>Gene structures of a representative sample of VuSDG (<b>A</b>) and VuJMJ (<b>B</b>) genes. Different classes of VuSDGs and VuJMJs are labeled within boxes on the left, separated by light gray dotted lines, with colors corresponding to each specific class. Coding sequences (CDS), untranslated regions (UTRs), and introns are depicted as green rectangles, red rectangles, and black lines, respectively. The genes displayed were randomly selected to represent each class of VuSDGs and VuJMJs identified in the cowpea genome.</p>
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<p>Quantification of the primary expansion mechanisms for the various classes/groups of VuSDG (<b>A</b>) and VuJMJ (<b>B</b>) genes identified in the cowpea genome. The <span class="html-italic">x</span>-axis represents the gene classes, and the <span class="html-italic">y</span>-axis shows the corresponding quantities for each class.</p>
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<p>Quantification of conserved loci for histone methyltransferases (<b>A</b>) and histone demethylases (<b>B</b>) among the 20 plant species with the highest orthology indices relative to the VuSDGs and VuJMJs. The plant families are represented by letters adjacent to the bars as follows: Fa (Fabaceae), Ma (Malvaceae), As (Salicaceae), Ru (Rutaceae), Vi (Vitaceae), Po (Poaceae), Br (Brassicaceae), Eu (Euphorbiaceae), Ph (Phirmaceae), Ro (Rosaceae), Ra (Ranunculaceae), Ap (Apiaceae), Cr (Crassulaceae), Li (Linaceae), So (Solanaceae), and Am (Amaranthaceae). The analyzed species are indicated within the bars, each distinguished by different colors. The percentages of gene orthology for each analyzed species in relation to cowpea are presented above the bars.</p>
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<p>Localization and identification of CCREs, presentation of consensus motifs, and associated transcription factors in the promoters of a representative sample containing 15 VuSDG genes. The gene sample plotted was randomly selected to display all the CCREs identified within the total set of analyzed promoters. <b>Legend:</b> In the upper panel, each analyzed promoter displays the motifs in their entirety. The central panel presents the consensus of the motifs along with their respective symbols (colors). In the lower panel, bona fide CCREs are represented by rectangles of varying colors, illustrating their corresponding associated transcription factors.</p>
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<p>Localization and identification of CCREs, presentation of consensus motifs, and associated transcription factors in the promoters of a representative sample containing 11 VuJMJ genes. The genes plotted were randomly selected to display all the CCREs identified within the total set of analyzed promoters. <b>Legend</b>: In the upper panel, each analyzed promoter displays the motifs in their entirety. The central panel presents the consensus of the motifs along with their respective symbols (colors). In the lower panel, bona fide CCREs are represented by red rectangles, illustrating their corresponding associated transcription factor.</p>
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<p>Heat map illustrating the modulation of transcripts encoding VuSDGs (<b>A</b>) and VuJMJs (<b>B</b>) that were differentially expressed in at least one treatment time point (RD25 or RD150) following root dehydration application.</p>
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<p>Relative expression of differentially expressed VuSDG and VuJMJ transcripts as determined via qPCR. <b>Legend:</b> The asterisk (*) indicates differentially expressed transcripts with <span class="html-italic">p</span> &lt; 0.05. Transcript identifiers are listed vertically below the bars as follows: VuSDG<sup>b</sup> (Vu167285|c0_g1_i8); VuJMJ<sup>a</sup> (Vu90804|c0_g1_i1); VuJMJ<sup>b</sup> (Vu2751|c0_g1_i2); VuJMJ<sup>c</sup> (Vu102353|c0_g1_i11); VuJMJ<sup>d</sup> (Vu88045|c0_g1_i9); VuJMJ<sup>e</sup> (Vu102353|c0_g1_i2); VuJMJ<sup>f</sup> (Vu19410|c1_g1_i1); VuJMJ<sup>g</sup> (Vu113817|c4_g2_i10); VuJMJ<sup>h</sup> (Vu113817|c4_g2_i3).</p>
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23 pages, 1289 KiB  
Review
Insight into the Mechanisms and Clinical Relevance of Antifungal Heteroresistance
by Yanyu Su, Yi Li, Qiaolian Yi, Yingchun Xu, Tianshu Sun and Yingxing Li
J. Fungi 2025, 11(2), 143; https://doi.org/10.3390/jof11020143 - 13 Feb 2025
Abstract
Antifungal resistance poses a critical global health threat, particularly in immuno-compromised patients. Beyond the traditional resistance mechanisms rooted in heritable and stable mutations, a distinct phenomenon known as heteroresistance has been identified, wherein a minority of resistant fungal cells coexist within a predominantly [...] Read more.
Antifungal resistance poses a critical global health threat, particularly in immuno-compromised patients. Beyond the traditional resistance mechanisms rooted in heritable and stable mutations, a distinct phenomenon known as heteroresistance has been identified, wherein a minority of resistant fungal cells coexist within a predominantly susceptible population. Heteroresistance may be induced by pharmacological factors or non-pharmacological agents. The reversible nature of it presents significant clinical challenges, as it can lead to undetected resistance during standard susceptibility testing. As heteroresistance allows fungal pathogens to survive antifungal treatment, this adaptive strategy often leads to treatment failure and recurring infection. Though extensively studied in bacteria, limited research has explored its occurrence in fungi. This review summarizes the current findings on antifungal heteroresistance mechanisms, highlighting the clinical implications of fungal heteroresistance and the pressing need for deeper mechanism insights. We aim to bring together the latest research advances in the field of antifungal heteroresistance, summarizing in detail its known characteristics, inducing factors, molecular mechanisms, and clinical significance, and describing the similarities and differences between heteroresistance, tolerance and persistence. Further research is needed to understand this phenomenon and develop more effective antifungal therapies to combat fungal infections. Full article
(This article belongs to the Section Fungal Pathogenesis and Disease Control)
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<p>Antifungal mode of action of azoles, AMB, 5-FC and echinocandins. Azoles inhibit ergosterol biosynthesis by targeting sterol 14α-demethylase, producing toxic 14-methyl sterols, altering fungal cell membrane permeability and metabolic state [<a href="#B74-jof-11-00143" class="html-bibr">74</a>,<a href="#B75-jof-11-00143" class="html-bibr">75</a>]. Echinocandins target β-1,3-glucan synthase, impairing fungal cell wall integrity and stress resistance [<a href="#B76-jof-11-00143" class="html-bibr">76</a>]. Amphotericin B binds to membrane ergosterol, forming pores that disrupt permeability and cause reactive oxygen species (ROS) accumulation, leading to cell death [<a href="#B77-jof-11-00143" class="html-bibr">77</a>,<a href="#B78-jof-11-00143" class="html-bibr">78</a>,<a href="#B79-jof-11-00143" class="html-bibr">79</a>]. 5-FC is converted intracellularly to 5-FU, inhibiting DNA and RNA synthesis [<a href="#B80-jof-11-00143" class="html-bibr">80</a>,<a href="#B81-jof-11-00143" class="html-bibr">81</a>].</p>
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<p>(By Figdraw 2.0). Explanation of resistance, susceptible, tolerance, persistence and heteroresistance from a cell population perspective. The state of cell proliferation is represented by the number of cells in the figure. Individual colors indicate different genotypes and phenotypes, which are red (genetically stable resistant), blue (susceptible), yellow (genetically unstable or phenotypically resistant), green (phenotypically tolerant).</p>
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26 pages, 6284 KiB  
Article
Proteomic Analysis of Plants with Binding Immunoglobulin Protein Overexpression Reveals Mechanisms Related to Defense Against Moniliophthora perniciosa
by Grazielle da Mota Alcântara, Gláucia Carvalho Barbosa Silva, Irma Yuliana Mora Ocampo, Amanda Araújo Kroger, Rafaelle Souza de Oliveira, Karina Peres Gramacho, Carlos Priminho Pirovani and Fátima Cerqueira Alvim
Plants 2025, 14(4), 503; https://doi.org/10.3390/plants14040503 - 7 Feb 2025
Abstract
Moniliophthora perniciosa is one of the main pathogens affecting cocoa, and controlling it generally involves planting resistant genotypes followed by phytosanitary pruning. The identification of plant genes related to defense mechanisms is crucial to unravel the molecular basis of plant–pathogen interactions. Among the [...] Read more.
Moniliophthora perniciosa is one of the main pathogens affecting cocoa, and controlling it generally involves planting resistant genotypes followed by phytosanitary pruning. The identification of plant genes related to defense mechanisms is crucial to unravel the molecular basis of plant–pathogen interactions. Among the candidate genes, BiP stands out as a molecular chaperone located in the endoplasmic reticulum that facilitates protein folding and is induced under stress conditions, such as pathogen attacks. In this study, the SoyBiPD gene was expressed in Solanum lycopersicum plants and the plants were challenged with M. perniciosa. The control plants exhibited severe symptoms of witches’ broom disease, whereas the transgenic lines showed no or mild symptoms. Gel-free proteomics revealed significant changes in the protein profile associated with BiP overexpression. Inoculated transgenic plants had a higher abundance of resistance-related proteins, such as PR2, PR3, and PR10, along with increased activity of antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), guaiacol peroxidase, and fungal cell wall-degrading enzymes (glucanases). Additionally, transgenic plants accumulated less H2O2, indicating more efficient control of reactive oxygen species (ROS). The interaction network analysis highlighted the activation of defense-associated signaling and metabolic pathways, conferring a state of defensive readiness even in the absence of pathogens. These results demonstrate that BiP overexpression increases the abundance of defense proteins, enhances antioxidant capacity, and confers greater tolerance to biotic stress. This study demonstrates the biotechnological potential of the BiP gene for genetic engineering crops with increased resistance to economically important diseases, such as witches’ broom in cocoa. Full article
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<p>Witches’ broom symptoms in <span class="html-italic">Solanun lycopersicum</span> inoculated with <span class="html-italic">Moniliophthora perniciosa</span> (<span class="html-italic">Mp</span>). Inoculated NT plants developed typical disease symptoms, such as hyperplasia and overgrowth (red arrows). In contrast, inoculated transgenic plants (BiP L12) showed no symptoms throughout the observation period. Non-inoculated plants (NT and BiP L12) maintained a healthy phenotype throughout the experiment. Pictures taken 15, 30, and 45 days after inoculation.</p>
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<p>Protein identification: NT—non-inoculated vs. L12 BiP-non-inoculated treatment (<span class="html-italic">p</span>-value &lt; 0.05 and |fold-change| &gt; 1.5)—(<b>A</b>) Heatmap of <span class="html-italic">Solanum lycopersicum</span> leaf protein abundance (log <sub>10</sub>). Indicated by the scale in the figure: proteins (rows) and treatments (columns). The dendrogram shows proteins grouped according to the Euclidean distance. (<b>B</b>) Molecular function and biological process. NT, non-transformed plant line; L12 BiP, plant line overexpressing BiP.</p>
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<p>Protein identification: NT-inoculated vs. L12 BiP-inoculated treatment (<span class="html-italic">p</span>-value &lt; 0.05 and |fold-change| &gt; 1.5)—(<b>A</b>) Heatmap of <span class="html-italic">Solanum lycopersicum</span> leaf protein abundance (log<sub>10</sub>). Indicated by the scale in the figure: proteins (rows) and treatments (columns). The dendrogram shows proteins grouped according to the Euclidean distance. (<b>B</b>) Molecular function and biological process.</p>
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<p>Abundance of identified proteins known to be involved in defense and stress response. In (A), a comparison of protein abundance identified in NT x L12 BiP plants not inoculated with <span class="html-italic">M. perniciosa</span>. In (B), a comparison of protein abundance identified in NT x vs. L12 BiP plants inoculated with <span class="html-italic">M. perniciosa</span>.</p>
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<p>Protein–protein interaction network identified in the <span class="html-italic">Solanum lycopersicum</span> leaf samples. Non-inoculated NT treatment vs. non-inoculated L12 BiP. (CL1–CL 21) clusters. The betweenness value is represented by the fill color of the nodes, where the lighter color represents the lowest value and the darker color the highest value. The node degree parameter is represented by the edge width of the nodes, where nodes with a thinner edge have a lower node degree and nodes with a wider edge have a higher node degree value.</p>
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<p>Protein–protein interaction network identified from <span class="html-italic">Solanum lycopersicum</span> leaf samples. Treatment NT inoculated vs. L12 BiP inoculated. (CL1–CL24) clusters. The betweenness value is represented by the fill color of the nodes, where the lighter color represents the lowest value and the darker color the highest value. The node degree parameter is represented by the edge width of the nodes, where nodes with a thin edge have a lower node degree and nodes with a wide edge have a higher node degree value.</p>
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<p>Immunodetection. (<b>A</b>) anti-BiP, (<b>B</b>) anti-catalase, (<b>C</b>) anti-PR2 and (<b>D</b>) anti-PR3 in non-transformed (NT) and transgenic (L2, L9 and L12) <span class="html-italic">Solanum lycopersicum</span> lines, non-inoculated and inoculated (NT I, L2 I, L9 I and L12 I) with <span class="html-italic">M. perniciosa</span>. (kDa) corresponds to the molecular mass (M) molecular marker; (1) protein accumulation; (2) mirror gel; (3) quantification of protein accumulation of the samples estimated through the Gel Quant. NET v1.8 program. Letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Analysis of H<sub>2</sub>O<sub>2</sub> production and peroxide activity in tomato leaves. NT (non-transformed) (T) trangenics plants and transgenic lines (L2, L9, and L12), with or without inoculation with Moniliophthora perniciosa. (<b>A</b>) Quantification of H<sub>2</sub>O<sub>2</sub> (mmol/g) in leaves of different tomato lines, with or without inoculation. Bars represent the mean ± standard error, and letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) Images of leaves treated with DAB (3,3′-diaminobenzidine), showing peroxide staining in response to inoculation with M. perniciosa (inoculated) and the control (non-inoculated). The leaves were treated with either H<sub>2</sub>O or DAB as indicated.</p>
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<p>Enzymatic activity analysis of tomato leaves. NT (non-transformed) and transgenic lines (L2, L9, and L12), with or without inoculation with Moniliophthora perniciosa. (<b>A</b>) Superoxide dismutase (SOD) activity (UA at 1 mM) in different tomato lines, with or without inoculation. Bars represent the mean ± standard error, and letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) Guaiacol peroxidase (GPX) activity (nmol glutathione·g<sup>−1</sup>·min<sup>−1</sup>·MF) in the same lines, with or without inoculation. (<b>C</b>) β-1,3-glucanase activity (µmol glucose/g MS) in leaves of the different lines, with or without inoculation.</p>
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<p>Biological model of the impact of BiP gene overexpression on the proteome of tomato plants (<span class="html-italic">S. lycopersicum</span>) under non-inoculated and <span class="html-italic">M. perniciosa</span>-inoculated conditions. NT (non-transformed) and transgenic lines (L12BiP), with and without inoculation with <span class="html-italic">Moniliophthora perniciosa</span>. (Red arrow) corresponds to proteins exclusive to the treatment, (Yellow arrow) corresponds to differentially abundant proteins between treatments). (NT without inoculation: General condition: basal activity focused on basic metabolic functioning. (L12BiP without inoculation). General condition: enhanced pre-established defense with metabolic and redox readiness, providing greater initial response capacity. (NT inoculated): General condition: dependence on photosynthetic processes with limited oxidative and local defensive responses. (L12BiP inoculated): General condition: robust systemic response integrated with the activation of multiple defense mechanisms, offering greater efficiency in combating the pathogen and protecting plant tissues.</p>
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22 pages, 6295 KiB  
Article
Discovery of Biofilm-Inhibiting Compounds to Enhance Antibiotic Effectiveness Against M. abscessus Infections
by Elizaveta Dzalamidze, Mylene Gorzynski, Rebecca Vande Voorde, Dylan Nelson and Lia Danelishvili
Pharmaceuticals 2025, 18(2), 225; https://doi.org/10.3390/ph18020225 - 7 Feb 2025
Abstract
Background/Objectives: Mycobacterium abscessus (MAB) is a highly resilient pathogen that causes difficult-to-treat pulmonary infections, particularly in individuals with cystic fibrosis (CF) and other underlying conditions. Its ability to form robust biofilms within the CF lung environment is a major factor contributing to [...] Read more.
Background/Objectives: Mycobacterium abscessus (MAB) is a highly resilient pathogen that causes difficult-to-treat pulmonary infections, particularly in individuals with cystic fibrosis (CF) and other underlying conditions. Its ability to form robust biofilms within the CF lung environment is a major factor contributing to its resistance to antibiotics and evasion of the host immune response, making conventional treatments largely ineffective. These biofilms, encased in an extracellular matrix, enhance drug tolerance and facilitate metabolic adaptations in hypoxic conditions, driving the bacteria into a persistent, non-replicative state that further exacerbates antimicrobial resistance. Treatment options remain limited, with multidrug regimens showing low success rates, highlighting the urgent need for more effective therapeutic strategies. Methods: In this study, we employed artificial sputum media to simulate the CF lung environment and conducted high-throughput screening of 24,000 compounds from diverse chemical libraries to identify inhibitors of MAB biofilm formation, using the Crystal Violet (CV) assay. Results: The screen established 17 hits with ≥30% biofilm inhibitory activity in mycobacteria. Six of these compounds inhibited MAB biofilm formation by over 60%, disrupted established biofilms by ≥40%, and significantly impaired bacterial viability within the biofilms, as confirmed by reduced CFU counts. In conformational assays, select compounds showed potent inhibitory activity in biofilms formed by clinical isolates of both MAB and Mycobacterium avium subsp. hominissuis (MAH). Key compounds, including ethacridine, phenothiazine, and fluorene derivatives, demonstrated potent activity against pre- and post-biofilm conditions, enhanced antibiotic efficacy, and reduced intracellular bacterial loads in macrophages. Conclusions: This study results underscore the potential of these compounds to target biofilm-associated resistance mechanisms, making them valuable candidates for use as adjuncts to existing therapies. These findings also emphasize the need for further investigations, including the initiation of a medicinal chemistry campaign to leverage structure–activity relationship studies and optimize the biological activity of these underexplored class of compounds against nontuberculous mycobacterial (NTM) strains. Full article
(This article belongs to the Topic Challenges and Future Prospects of Antibacterial Therapy)
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<p>The development of the HTS assay for the identification of compounds with potency against mycobacterial biofilms. (<b>A</b>) The CV absorbance readings were recorded for MAB grown in SCFM over time and across a range of concentrations in the 384WP format. (<b>B</b>) The percentage of biofilm formation on day five was calculated as described in <a href="#sec4-pharmaceuticals-18-00225" class="html-sec">Section 4</a> using DMSO- and antibiotic-treated wells as controls for bacterial growth and inhibition in the 384WP format. (<b>C</b>) MAB viability was recorded as fluorescent readings for the control and antibiotic-treated groups, based on resazurin reduction rates in the 96WP format. (<b>D</b>) The percentage of MAB biofilm formation on day 5 was determined based on the CV assay. Data are expressed as the means ± standard deviations (SD) of three independent experiments. The statistical significance between the DMSO and antibiotic-treated groups is indicated as follows: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Activity of selected hit compounds against biofilms of MAB19977 under pre- and post-formation conditions. (<b>A</b>) Prevention of biofilm formation (pre-exposure condition). (<b>B</b>) Inhibition of 24 h pre-formed biofilms (post-exposure condition). Data are presented as percentage of inhibition in bacterial viability in biofilms or biomass relative to untreated control biofilms, based on three independent biological replicates.</p>
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<p>The screening of antibiofilm activity across NTM strains. Biofilm biomass was quantified using crystal violet staining under pre-treatment conditions with select hit compounds at a 100 μM concentration, as described in the Materials and Methods. DMSO and CLA70 served as growth and inhibition controls, respectively. Data are presented as the mean ± standard deviation from three independent replicates.</p>
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<p>The in vitro time-kill dynamics of mycobacteria during treatment with select hit compounds and in combination with an antibiotic. (<b>A</b>) MAB19977 survival rates in AMK, compound, and compound–AMK treatment groups over 5 days in 7H9 broth. (<b>B</b>) MAH104 survival rates in AMK, compound, and compound–AMK combination treatment groups over 5 days in 7H9 broth. Bacterial CFUs were recorded after treatment with the antibiotic at 2× the MIC and/or compounds at IC<sub>50</sub> concentrations. Survival percentages were calculated relative to the DMSO growth control (untreated). Antimicrobials were added at time zero and then supplemented every other day throughout the duration of the experiment. Data are presented as means ± standard deviations (SD) from two independent experiments performed in triplicate. The statistical significance between the compound group alone and antibiotic–compound combination treatment groups on day 5 is indicated as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The quantification of bacterial CFUs for establishing the intracellular potency of hit compounds in infected THP-1 macrophages. Compounds at their highest nontoxic concentrations (as listed in <a href="#pharmaceuticals-18-00225-t003" class="html-table">Table 3</a>) were added to (<b>A</b>) MAB19977-infected or (<b>B</b>) MAH104-infected THP-1 cell monolayers at 2 h post-infection and, subsequently, every other day throughout the duration of the experiment. Bacterial CFUs were determined by lysing the cells with 0.1% Triton X-100 on day 5 for both MAB19977 and MAH104, followed by plating serial dilutions on 7H10 agar plates. The percentage of surviving bacteria was calculated by dividing the CFUs per well for each treatment group by the CFUs in the DMSO growth control, which represented a 100% bacterial survival. AMK and CLA at a bactericidal concentration of 32 µg/mL and 4 µg/mL, respectively, were used as controls to assess the growth inhibition of MAB19977 and MAH104 within macrophages. The tested compounds demonstrated a statistically significant reduction in intracellular bacterial survival as determined by one-way ANOVA with multiple comparisons between the DMSO control and compound-treated groups. The significance between the DMSO control group and compound treatment groups on day 5 is indicated as ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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15 pages, 1003 KiB  
Review
Adaptable Alchemy: Exploring the Flexibility of Specialized Metabolites to Environmental Perturbations Through Post-Translational Modifications (PTMs)
by Luca Cimmino, Annalisa Staiti, Domenico Carputo, Teresa Docimo, Vincenzo D’Amelia and Riccardo Aversano
Plants 2025, 14(3), 489; https://doi.org/10.3390/plants14030489 - 6 Feb 2025
Abstract
Plants are subjected to various stresses during the growth process, including biotic stresses, as well as abiotic stresses such as temperature, drought, salt, and heavy metals. To cope with these biotic and abiotic adversities, plants have evolved complex regulatory mechanisms during their long-term [...] Read more.
Plants are subjected to various stresses during the growth process, including biotic stresses, as well as abiotic stresses such as temperature, drought, salt, and heavy metals. To cope with these biotic and abiotic adversities, plants have evolved complex regulatory mechanisms during their long-term environmental adaptations. In a suddenly changing environment, protein modifiers target other proteins to induce post-translational modification (PTM) in order to maintain cell homeostasis and protein biological activity in plants. PTMs modulate the activity of enzymes and transcription factors in their respective metabolic pathways, enabling plants to produce essential compounds for their survival under stress conditions. Examples of post-translational mechanisms include phosphorylation, ubiquitination, glycosylation, acetylation, protein–protein interactions, and targeted protein degradation. Furthermore, the role of histone modifications in regulating secondary metabolism deserves attention due to its potential impact on heritability and its contribution to stress tolerance. Understanding the epigenetic aspect of these modifications can provide valuable insights into the mechanisms underlying stress response. In this context, also examining PTMs that impact the biosynthesis of secondary metabolites is meaningful. Secondary metabolites encompass a wide range of compounds such as flavonoids, alkaloids, and terpenoids. These secondary metabolites play a crucial role in plant defense against herbivores, pathogens, and oxidative stress. In this context, it is imperative to understand the contribution of secondary metabolism to plant tolerance to abiotic stresses and how this understanding can be leveraged to improve long-term survival. While many studies have focused on the transcriptional regulation of these metabolites, there is a growing interest in understanding various changes in PTMs, such as acetylation, glycosylation, and phosphorylation, that are able to modulate plants’ response to environmental conditions. In conclusion, a comprehensive exploration of post-translational mechanisms in secondary metabolism can enhance our understanding of plant responses to abiotic stress. This knowledge holds promise for future applications in genetic improvement and breeding strategies aimed at increasing plant resilience to environmental challenges. Full article
(This article belongs to the Special Issue Protein Metabolism in Plants and Algae under Abiotic Stress)
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<p>Abiotic factors trigger post-translational modifications (PTMs) in plants, influencing chromatin structure and gene expression. An example of anthocyanin biosynthesis is presented: on the left, acetylation (Ac) opens chromatin, allowing transcriptional activation of the MBW complex and downstream enzymes (<span class="html-italic">DFR</span>, <span class="html-italic">ANS</span>, <span class="html-italic">UGT</span>), leading to anthocyanin production. On the right, methylation (Me) condenses chromatin, inhibiting transcription of the same biosynthetic pathway, preventing anthocyanin accumulation. This highlights how PTMs, such as acetylation and methylation, regulate chromatin dynamics in response to environmental stimuli. Created in BioRender: <a href="https://BioRender.com/k98j551" target="_blank">https://BioRender.com/k98j551</a> (accessed on 2 December 2024).</p>
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<p>Metabolites as post-translational modification (PTM) effectors in root growth regulation. In wild-type plants, flavanols and anthocyanins regulate the balance between cell division and differentiation in root meristems. Light induces asymmetric accumulation of flavonols and anthocyanins on the exposed side (depicted in purple in the image on the left), promoting cell differentiation and limiting proliferation. This results in light avoidance by the roots, leading to reduced growth and a shift in the meristem away from the light source. Flavonols inhibit auxin polar transport mediated by PIN1 and scavenge the superoxide anion (O<sup>2−</sup>), contributing to the regulation of the boundary between the proliferative and differentiation zones. In the <span class="html-italic">tt4</span> mutant, which does not produce anthocyanins, the light response is impaired. In the absence of anthocyanins, the roots do not asymmetrically accumulate flavonols and do not exhibit light avoidance. Consequently, the root growth in the <span class="html-italic">tt4</span> mutant is greater compared to the wild type, with a regular zonation between cell proliferation and differentiation, as the roots do not limit growth in response to light. This highlights the importance of anthocyanins and flavonols inducing altered growth responses under light conditions. Created in BioRender: <a href="https://BioRender.com/p40c968" target="_blank">https://BioRender.com/p40c968</a> (accessed on 2 December 2024).</p>
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19 pages, 4424 KiB  
Article
Fatty Acid ABCG Transporter GhSTR1 Mediates Resistance to Verticillium dahliae and Fusarium oxysporum in Cotton
by Guanfu Cheng, Xiuqing Li, W. G. Dilantha Fernando, Shaheen Bibi, Chunyan Liang, Yanqing Bi, Xiaodong Liu and Yue Li
Plants 2025, 14(3), 465; https://doi.org/10.3390/plants14030465 - 5 Feb 2025
Abstract
Verticillium wilt and Fusarium wilt cause significant losses in cotton (Gossypium hirsutum) production and have a significant economic impact. This study determined the functional role of GhSTR1, a member of the ABCG subfamily of ATP-binding cassette (ABC) transporters, that mediates [...] Read more.
Verticillium wilt and Fusarium wilt cause significant losses in cotton (Gossypium hirsutum) production and have a significant economic impact. This study determined the functional role of GhSTR1, a member of the ABCG subfamily of ATP-binding cassette (ABC) transporters, that mediates cotton defense responses against various plant pathogens. We identified GhSTR1 as a homolog of STR1 from Medicago truncatula and highlighted its evolutionary conservation and potential role in plant defense mechanisms. Expression profiling revealed that GhSTR1 displays tissue-specific and spatiotemporal dynamics under stress conditions caused by Verticillium dahliae and Fusarium oxysporum. Functional validation using virus-induced gene silencing (VIGS) showed that silencing GhSTR1 improved disease resistance, resulting in milder symptoms, less vascular browning, and reduced fungal growth. Furthermore, the AtSTR1 loss-of-function mutant in Arabidopsis thaliana exhibited similar resistance phenotypes, highlighting the conserved regulatory role of STR1 in pathogen defense. In addition to its role in disease resistance, the mutation of AtSTR1 in Arabidopsis also enhanced the vegetative and reproductive growth of the plant, including increased root length, rosette leaf number, and plant height without compromising drought tolerance. These findings suggest that GhSTR1 mediates a trade-off between defense and growth, offering a potential target for optimizing both traits for crop improvement. This study identifies GhSTR1 as a key regulator of plant–pathogen interactions and growth dynamics, providing a foundation for developing durable strategies to enhance cotton’s resistance and yield under biotic and abiotic stress conditions. Full article
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<p>Cloning, structural analysis, and phylogenetic relationships of <span class="html-italic">GhSTR1.</span> (<b>a</b>) PCR amplification of the <span class="html-italic">GhSTR1</span> gene. The red arrow indicates the target band at the expected size of 2454 bp, confirming the successful cloning of the <span class="html-italic">GhSTR1</span> coding sequence (CDS). (<b>b</b>) Protein domain comparison. SMART-based domain predictions showed that <span class="html-italic">GhSTR1</span>, <span class="html-italic">MtSTR1</span>, and <span class="html-italic">AtSTR1</span> share a conserved AAA ATPase domain (red oval) and transmembrane helices (blue rectangles), which are characteristic features of the ABCG subfamily. (<b>c</b>) The phylogenetic analysis of GhSTR1 was conducted using MEGA11 to study the primary ABC transporter proteins from <span class="html-italic">Carya illinoinensis</span> (pecan), <span class="html-italic">Juglans regia</span> (walnut), <span class="html-italic">Alnus glutinosa</span> (alder), <span class="html-italic">Theobroma cacao</span> (cacao), <span class="html-italic">Citrus x clementina</span> (clementine<span class="html-italic">), Prunus avium</span> (cherry), and <span class="html-italic">Ricinus communis</span> (castor bean). The evolutionary relationships among these major ABC transporter proteins were analyzed using the Neighbor-Joining (NJ) method and the JTT substitution model in MEGA11 software (The red section of the figure illustrates the cotton proteins and their corresponding protein families analyzed in this study). Bootstrap analyses with 1000 replications were performed on the nodes of the phylogenetic tree to evaluate their statistical support. As shown in <a href="#plants-14-00465-f001" class="html-fig">Figure 1</a>c, the statistical support for key nodes confirms the robustness of the inferred evolutionary relationships. The phylogenetic tree indicates that GhSTR1 is closely related to MtSTR1 and AtSTR1, confirming its classification within the ABCG subfamily.</p>
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<p>Transcript levels of <span class="html-italic">GhSTR1</span> under <span class="html-italic">Verticillium dahliae</span> V991 and <span class="html-italic">Fusarium oxysporum</span> St89 stress. (<b>a</b>,<b>c</b>) Relative expression levels of <span class="html-italic">GhSTR1</span> in leaves under stress from <span class="html-italic">V. dahliae</span> V991 and <span class="html-italic">F. oxysporum</span> St89, respectively. (<b>b</b>,<b>d</b>) Relative expression levels of <span class="html-italic">GhSTR1</span> in roots under stress from <span class="html-italic">V. dahliae</span> V991 and <span class="html-italic">F. oxysporum</span> St89, respectively. Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 3) and normalized to the control group (CK, sterile water treatment). Statistical analysis was conducted using the <span class="html-italic">t</span>-test, with significance indicated as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Silencing efficiency of the <span class="html-italic">GhSTR1</span> gene. (<b>a</b>) PCR amplification of the <span class="html-italic">GhSTR1</span> target fragment. (<b>b</b>) Restriction digestion of the TRV vector, confirming successful vector construction. (<b>c</b>) The bleaching phenotype observed in pTRV2::<span class="html-italic">GhCLA1</span>-silenced cotton plants, demonstrating effective gene silencing. (<b>d</b>,<b>e</b>) Relative expression levels of <span class="html-italic">GhCLA1</span> and <span class="html-italic">GhSTR1</span> in pTRV2::<span class="html-italic">00</span> and pTRV2::<span class="html-italic">GhSTR1</span> plants, respectively. Data are presented as the mean ± standard error (<span class="html-italic">n</span> = 3). Statistical significance is indicated as follows: *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of <span class="html-italic">GhSTR1</span> gene silencing in cotton resistance to <span class="html-italic">V. dahliae</span> V991 and <span class="html-italic">F. oxysporum</span> St89. (<b>a</b>,<b>f</b>) Leaves of pTRV2::<span class="html-italic">GhSTR1</span> plants exhibited more severe chlorosis, wilting, and lesions following infection with <span class="html-italic">V. dahliae</span> (V991) and <span class="html-italic">F. oxysporum</span> (St89) compared to WT and pTRV2::<span class="html-italic">00</span> controls, respectively. Scale bar = 2 cm. (<b>b</b>,<b>g</b>) Longitudinal sections of infected stems showed more pronounced vascular browning in pTRV2::<span class="html-italic">GhSTR1</span> plants, indicating greater pathogen invasion. Scale bar = 0.2 cm. (<b>c</b>,<b>h</b>) Disease index analysis at 20 dpi revealed significantly higher indices in pTRV2::<span class="html-italic">GhSTR1</span> plants than WT and pTRV2::<span class="html-italic">00</span> control. (<b>d</b>,<b>i</b>) qRT-PCR analysis showed significantly higher fungal biomass in pTRV2::<span class="html-italic">GhSTR1</span> plants than in the controls. (<b>e</b>,<b>j</b>) Fungal hyphal growth in stem sections (1 cm above the cotyledonary node) cultured on PDA medium was significantly greater in pTRV2::<span class="html-italic">GhSTR1</span> plants than in the controls. Scale bar = 0.2 cm. Each group included ≥30 plants with 3 replicates to ensure result reliability. Data are expressed as the mean ± standard error (<span class="html-italic">n</span> = 3). Statistical significance was assessed using analysis of variance (ANOVA), followed by Duncan’s multiple comparison test. The significance levels are indicated as follows: ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001. Different groups with different letters represent statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Genotypic validation and expression analysis of <span class="html-italic">AtSTR1</span> T-DNA insertion mutant in <span class="html-italic">Arabidopsis thaliana.</span> (<b>a</b>) Schematic representation of <span class="html-italic">AtSTR1</span> gene structure in the SALK_129014 mutant. The promoter is shown as an orange rectangle, the single exon as a yellow rectangle, and the T-DNA insertion site as a blue inverted triangle. (<b>b</b>) Genotyping results for the homozygous SALK_129014 mutant. Homozygous plants lacked amplification with LP/RP primers but showed a T-DNA-specific fragment with LBa1/RP primers. (<b>c</b>) SqRT-PCR showed reduced <span class="html-italic">AtSTR1</span> expression in the <span class="html-italic">Atstr1</span> mutant compared to that in the wild-type plants. <span class="html-italic">Actin2</span> was used as the reference gene for normalization. (<b>d</b>) qRT-PCR confirmed significantly reduced <span class="html-italic">AtSTR1</span> expression in the <span class="html-italic">Atstr1</span> mutant relative to the wild-type plants. Data are presented as mean ± standard error (<span class="html-italic">n</span> = 3), with statistical significance indicated as follows: *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Enhanced resistance of <span class="html-italic">Atstr1</span> mutant to <span class="html-italic">V. dahliae</span> (V991) and <span class="html-italic">F. oxysporum</span> (St89). (<b>a</b>,<b>e</b>) Phenotypic comparison of <span class="html-italic">Arabidopsis thaliana</span> Col-0 wild-type and <span class="html-italic">Atstr1</span> mutant 15 days post-infection (dpi) with <span class="html-italic">V. dahliae</span> (V991) and <span class="html-italic">F. oxysporum</span> (St89), respectively. <span class="html-italic">Atstr1</span> mutant displayed reduced wilting and chlorosis compared to the wild-type plants. Scale bar = 1 cm. (<b>b</b>,<b>f</b>) Stem longitudinal sections 1 cm above the tillering node, showing vascular browning at 15 dpi with V991 and St89. <span class="html-italic">Atstr1</span> mutant exhibited milder vascular browning compared to the wild-type plants. Scale bar = 0.2 cm. (<b>c</b>,<b>g</b>) Disease index values at 15 dpi. <span class="html-italic">Atstr1</span> mutant showed significantly lower disease indices than the wild-type plants for both V991 and St89 infections. (<b>d</b>,<b>h</b>) qRT-PCR analysis of the fungal biomass at 15 dpi. <span class="html-italic">Atstr1</span> mutant exhibited significantly reduced fungal biomass compared to wild-type plants. Data are expressed as mean ± standard error (<span class="html-italic">n</span> = 3). Statistical significance was assessed using analysis of variance (ANOVA), followed by Duncan’s multiple comparison test. Significance levels are indicated as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Groups with different letters represent statistically significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Growth and development phenotype analysis of <span class="html-italic">Atstr1</span> mutant in <span class="html-italic">Arabidopsis thaliana.</span> (<b>a</b>) Overall developmental state of the wild-type (Col-0) and <span class="html-italic">Atstr1</span> mutant during the 15-day growth stage. Scale bar = 5 cm. (<b>b</b>) Root length measurements during the 15-day growth period. Scale bar = 1 cm. (<b>c</b>) Leaf size of the wild-type and <span class="html-italic">Atstr1</span> mutant during the 15-day growth stage. Scale bar = 2 mm. (<b>d</b>) Rosette leaf diameter during the 15-day growth stage. The average diameter was measured at the widest point of the leaf blade across all rosette leaves of the plant. (<b>e</b>) Rosette leaf number during the 15-day growth stage. (<b>f</b>) Overall developmental state of the wild-type and <span class="html-italic">Atstr1</span> mutant during the 45-day growth stage. Scale bar = 5 cm. (<b>g</b>) Plant height during the 45-day growth stage. (<b>h</b>) Number of bolted branches per plant during the 45-day growth stage. (<b>i</b>) Number of siliques per plant during the 45-day growth stage. Forty plants were analyzed for each treatment. Data are presented as the mean ± standard error (SEM; <span class="html-italic">n</span> = 3). The statistical significance is as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Phenotypic and physiological analysis of <span class="html-italic">Atstr1</span> mutant under drought stress. (<b>a</b>) Phenotypic comparison of <span class="html-italic">Arabidopsis thaliana</span> Col-0 wild-type and <span class="html-italic">Atstr1</span> mutant plants before drought treatment, after 10 days of drought stress, and following 8 days of rehydration. (<b>b</b>) Survival rate analysis of Col-0 and <span class="html-italic">Atstr1</span> mutant plants after drought stress and rehydration. (<b>c</b>) Water loss rate curves comparing Col-0 and <span class="html-italic">Atstr1</span> mutant plants during drought stress. A total of 40 plants were analyzed per treatment. Data are presented as mean ± standard error (SEM; <span class="html-italic">n</span> = 3). Statistical significance is as follows: ns: no significant difference.</p>
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17 pages, 4397 KiB  
Article
MaNrtB, a Putative Nitrate Transporter, Contributes to Stress Tolerance and Virulence in the Entomopathogenic Fungus Metarhizium acridum
by Jia Wang, Yuneng Zou, Yuxian Xia and Kai Jin
J. Fungi 2025, 11(2), 111; https://doi.org/10.3390/jof11020111 - 1 Feb 2025
Abstract
Nitrogen is an essential nutrient that frequently determines the growth rate of fungi. Nitrate transporter proteins (Nrts) play a crucial role in the cellular absorption of nitrate from the environment. Entomopathogenic fungi (EPF) have shown their potential in the biological control of pests. [...] Read more.
Nitrogen is an essential nutrient that frequently determines the growth rate of fungi. Nitrate transporter proteins (Nrts) play a crucial role in the cellular absorption of nitrate from the environment. Entomopathogenic fungi (EPF) have shown their potential in the biological control of pests. Thus, comprehending the mechanisms that govern the pathogenicity and stress tolerance of EPF is helpful in improving the effectiveness and practical application of these fungal biocontrol agents. In this study, we utilized homologous recombination to create MaNrtB deletion mutants and complementation strains. We systematically investigated the biological functions of the nitrate transporter protein gene MaNrtB in M. acridum. Our findings revealed that the disruption of MaNrtB resulted in delayed conidial germination without affecting conidial production. Stress tolerance assays demonstrated that the MaNrtB disruption strain was more vulnerable to UV-B irradiation, hyperosmotic stress, and cell wall disturbing agents, yet it exhibited increased heat resistance compared to the wild-type strain. Bioassays on the locust Locusta migratoria manilensis showed that the disruption of MaNrtB impaired the fungal virulence owing to the reduced appressorium formation on the insect cuticle and the attenuated growth in the locust hemolymph. These findings provide new perspectives for understanding the pathogenesis of EPF. Full article
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<p>Bioinformatics of <span class="html-italic">MaNrtB</span>. (<b>A</b>) Domain analysis of <span class="html-italic">MaNrtB</span>, which contains 12 transmembrane (TM) spanning alpha helices marked in a blue color. (<b>B</b>) A phylogenetic tree was constructed with the NrtB protein sequences of <span class="html-italic">Metarhizium acridum</span> (EFY90826.1), <span class="html-italic">Metarhizium anisopliae</span> (KAF5125400.1), <span class="html-italic">Metarhizium robertsii</span> (XP_007820472.2), <span class="html-italic">Fusarium oxysporum</span> (XP_018232784.1), <span class="html-italic">Fusarium redolens</span> (XP_046056618.1), <span class="html-italic">Pyricularia oryzae</span> (XP_003710887.1), <span class="html-italic">Verticillium dahlia</span> (KAF3360447.1), <span class="html-italic">Neurospora crassa</span> (XP_957430.2), <span class="html-italic">Aspergillus nidulans</span> (XP_658612.1), <span class="html-italic">Aspergillus fumigatus</span> (KAH1494629.1), <span class="html-italic">Aspergillus oryzae</span> (EIT78908.1), <span class="html-italic">Penicillium paradoxum</span> (XP_057035394.1), <span class="html-italic">Mycosarcoma maydis</span> (XP_011390345.1), <span class="html-italic">Blastobotrys adeninivorans</span> (CAQ77149.1), <span class="html-italic">Cyberlindnera americana</span> (QFR37177.1), <span class="html-italic">Hansenula polymorpha</span> (XP_018213600.1), and <span class="html-italic">Candida albicans</span> (XP_717110.1). The red triangle represents the NrtB homologous protein in <span class="html-italic">M. acridum</span>.</p>
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<p>Disruption and complementation of <span class="html-italic">MaNrtB</span>. (<b>A</b>,<b>B</b>) Schematic diagrams of the knockout and complementation vector constructions. Black arrows indicate the positions of the primers. (<b>C</b>) Verification of transformants by RT-qPCR. Error bars = mean ± SEM. Asterisks indicate a significant difference at (**) <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Deletion of <span class="html-italic">MaNrtB</span> affected the conidial germination but not the conidial yield. (<b>A</b>) The germination rate of each strain of <span class="html-italic">M. acridum</span> at different times. (<b>B</b>) The GT<sub>50</sub> of each strain during germination. (<b>C</b>) The conidia yield of each strain on 1/4 SDAY medium for 3 d, 6 d, 9 d, 12 d, and 15 d. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Deletion of <span class="html-italic">MaNrtB</span> reduced the fungal tolerance to UV-B irradiation but increased the fungal tolerance to heat shock. (<b>A</b>) Conidial germination treated with UV-B irradiation for 0.5 h, 1.0 h, 1.5 h, and 2.0 h. (<b>B</b>) The GT<sub>50</sub> under UV-B irradiation. (<b>C</b>) Germination rates treated with heat shock for 2.0 h, 4.0 h, 6.0 h, and 8.0 h. (<b>D</b>) GT<sub>50</sub> under heat shock. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Disruption of <span class="html-italic">MaNrtB</span> reduced tolerances to multiple chemical reagents. (<b>A</b>) Colony morphologies of <span class="html-italic">M. acridum</span> strains grown for 6 days on 1/4 SDAY with different chemical reagents. (<b>B</b>) The growth rates of <span class="html-italic">M. acridum</span> strains under different chemical reagents. (<b>C</b>) Relative growth inhibition rates of <span class="html-italic">M. acridum</span> strains under different chemical reagents. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Disruption of <span class="html-italic">MaNrtB</span> impaired fungal virulence. (<b>A</b>) Survival rates of locusts after topical application of fungal conidia. Liquid paraffin oil as a control. (<b>B</b>) LT<sub>50</sub> of fungal strains against locusts by topical application. (<b>C</b>) Survival rates of locusts after hemocoel injection of conidia. Sterile water as a control. (<b>D</b>) LT<sub>50</sub> of fungal strains against locusts by hemocoel injection. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Disruption of <span class="html-italic">MaNrtB</span> affected locust cuticle penetration of <span class="html-italic">M. acridum</span>. (<b>A</b>) Penetration assays. (<b>B</b>) The germination rate of the WT, Δ<span class="html-italic">MaNrtB</span> and CP strains growing on the hind wings of locust for 2 h, 4 h, 6 h, 8 h and 10 h. (<b>C</b>) The GT<sub>50</sub> of each strain on the hind wings of locust. (<b>D</b>) The appressorium formation rates of the WT, Δ<span class="html-italic">MaNrtB</span> and CP strains growing on the hind wings of locust for 12 h, 16 h, 20 h, 24 h, and 28 h. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Disruption of <span class="html-italic">MaNrtB</span> impaired fungal growth in locust hemolymph. (<b>A</b>) The biomass levels were quantified from the 3-day-old submerged cultures in CZA and three amended media, sucrose–peptone medium (SPM) and trehalose–peptone medium (TPM). (<b>B</b>) The relative expression of <span class="html-italic">Attacin</span> and <span class="html-italic">Defensin</span> in locust fat bodies was determined at 24 h after injection by RT-qPCR. Error bars = mean ± SEM. Asterisks indicate a significant difference at (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, or (ns) <span class="html-italic">p</span> &gt; 0.05.</p>
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30 pages, 6105 KiB  
Article
Genome-Wide Genetic Architecture for Common Scab (Streptomyces scabei L.) Resistance in Diploid Potatoes
by Bourlaye Fofana, Braulio Jorge Soto-Cerda, Mohsin Zaidi, David Main and Sherry Fillmore
Int. J. Mol. Sci. 2025, 26(3), 1126; https://doi.org/10.3390/ijms26031126 - 28 Jan 2025
Abstract
Most cultivated potato (Solanum tuberosum) varieties are highly susceptible to common scab (Streptomyces scabei). The disease is widespread in all major potato production areas and leads to high economic losses and food waste. Varietal resistance is seen as the [...] Read more.
Most cultivated potato (Solanum tuberosum) varieties are highly susceptible to common scab (Streptomyces scabei). The disease is widespread in all major potato production areas and leads to high economic losses and food waste. Varietal resistance is seen as the most viable and sustainable long-term management strategy. However, resistant potato varieties are scarce, and their genetic architecture and resistance mechanisms are poorly understood. Moreover, diploid potato relatives to commercial potatoes remain to be fully explored. In the current study, a panel of 384 ethyl methane sulfonate (EMS)-mutagenized diploid potato clones were evaluated for common scab coverage, severity, and incidence traits under field conditions, and genome-wide association studies (GWASs) were conducted to dissect the genetic architecture of their traits. Using the GAPIT-MLM and RTM-GWAS statistical models, and Mann–Whitney non-parametric U-tests, we show that 58 QTNs/QTLs distributed on all 12 potato chromosomes were associated with common scab resistance, 52 of which had significant allelic effects on the three traits. In total, 38 of the 52 favorable QTNs/QTLs were found to be pleiotropic on at least two of the traits, while 14 were unique to a single trait and were found distributed over 3 chromosomes. The identified QTNs/QTLs showed low to high effects, highlighting the quantitative and multigenic inheritance of common scab resistance. The QTLs/QTNs associated with the three common scab traits were found to be co-located in genomic regions carrying 79 candidate genes playing roles in plant defense, cell wall component biosynthesis and modification, plant–pathogen interactions, and hormone signaling. A total of 61 potato clones were found to be tolerant or resistant to common scab. Taken together, the data show that the studied germplasm panel, the identified QTNs/QTLs, and the candidate genes are prime genetic resources for breeders and biologists in breeding and targeted gene editing. Full article
(This article belongs to the Special Issue New Insights into Plant Pathology and Abiotic Stress)
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<p>(<b>A</b>–<b>E</b>) Common scab severity distribution among the 384 germplasm panel; (<b>A</b>) spatial visual distribution of individuals for scab disease severity rating classes; (<b>B</b>) scatter box plot of average distribution; (<b>C</b>) frequency distribution of individuals for scab severity ratings; (<b>D</b>) density plot of individuals for scab severity ratings; and (<b>E</b>) cummulative density distribution for scab severity ratings.</p>
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<p>(<b>A</b>–<b>E</b>) Common scab coverage phenotype distribution among the 384 germplasm panel; (<b>A</b>) spatial visual distribution of individuals for scab disease coverage rating classes; (<b>B</b>) scatter box plot of average distribution; (<b>C</b>) frequency distribution of individuals for scab coverage ratings; (<b>D</b>) density plot of individuals for scab coverage ratings; and (<b>E</b>) cummulative density dstribution for scab coverage ratings.</p>
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<p>(<b>A</b>–<b>E</b>) Common scab incidence distribution among the 384 germplasm panel; (<b>A</b>) spatial visual distribution of individuals for scab disease incidence rating classes; (<b>B</b>) scatter box plot of average distribution; (<b>C</b>) frequency distribution of individuals for scab incidence ratings; (<b>D</b>) density plot of individuals for scab incidence ratings; and (<b>E</b>) cummulative density disstribution for scab incidence ratings.</p>
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<p>Principal component analysis of the 2021–2023 dataset depicting the differentiation among the 68 clones most contrasting for common scab reaction. Three groups can be observed. Green: low rating for incidence, severity, and surface coverage. Red: high rating for incidence, severity, and surface coverage. Orange: low to medium rating. The red grouping is closely associated with high incidence, severity, and coverage. Note that the sample IDs may be found overlaped and are not intended to be readable in each group.</p>
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<p>(<b>A</b>–<b>C</b>) Manhattan and Q-Q plots showing QTNs/QTLs and chromosomal regions associated with common scab traits using the GAPIT-MLM. Each panel corresponds two datasets (mean and BLUP). (<b>A</b>) Coverage mean and BLUP; (<b>B</b>) severity mean and BLUP; and (<b>C</b>) incidence mean and BLUP. The dotted line indicates the cut off −log<sub>10</sub>(<span class="html-italic">p</span>) &lt; 2.5. The six boxed inserts on the right present the quantile–quantile (Q-Q) plot for each Manhattan plot, showing a well-fitted GWAS model, with minimal artifact bias from −log<sub>10</sub>(<span class="html-italic">p</span>) values &gt; 2.5. The blue dots represent the <span class="html-italic">p</span>-values observed from the genomic association study. The red line is the expected distribution of <span class="html-italic">p</span>-values when there is no association under the null hypothesis. It acts as a reference line to assess if the data deviate significantly from the expected distribution. Since most tested SNPs may not be associated with the trait, the majority of blue dots in the Q-Q plot should fall on the red line, indicative of a good fit to the null hypothesis, as shown. The deviations from the red line suggest potential significant associations. The gray area indicates the 95% confidence interval under the null.</p>
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<p>(<b>A</b>–<b>C</b>) Violin plots illustrating the phenotypic differences between potato genotypes carrying different alleles of the significant SNPs. (<b>A</b>) Scab incidence, (<b>B</b>) scab severity, and (<b>C</b>) scab coverage. Means and standard variations for each SNP allele are shown. Statistical differences between alleles were tested using the Mann–Whitney non-parametric U test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>,<b>B</b>) Heatmap and violin plots displaying positive and negative allelic effects among 40 potato clones with differential common scab reactions. (<b>A</b>) Heatmap displaying the distribution of 32 unique positive QTL (PQTL) alleles among 20 high and 20 low potato clones carrying more or fewer PQTLs; (<b>B</b>) violin plots illustrating the mean of favorable PQTL for the best potato genotypes carrying 18 PQTLs and the worst genotypes carrying 5.6 PQTLs for each common scab trait.</p>
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13 pages, 2852 KiB  
Article
Effect of Multiyear Biodegradable Plastic Mulch on Soil Microbial Community, Assembly, and Functioning
by Xiaowei Liu, Zongyu Wen, Wei Zhou, Wentao Dong, Huiqing Ren, Gang Liang and Wenwen Gong
Microorganisms 2025, 13(2), 259; https://doi.org/10.3390/microorganisms13020259 - 24 Jan 2025
Viewed by 257
Abstract
The increasing use of biodegradable plastic mulch like polybutylene adipate terephthalate (PBAT) has raised concerns about its long-term environmental impact. In this study, we investigated the effects of multiyear PBAT mulch application on bacterial and fungal communities, assembly mechanisms, and key ecological functions. [...] Read more.
The increasing use of biodegradable plastic mulch like polybutylene adipate terephthalate (PBAT) has raised concerns about its long-term environmental impact. In this study, we investigated the effects of multiyear PBAT mulch application on bacterial and fungal communities, assembly mechanisms, and key ecological functions. The microbial community diversity and composition were significantly altered after multiyear biodegradable plastic mulching. We observed that PBAT treatment enriched specific bacterial genera, such as Pantoea, potentially involved in plastic degradation, and fungal genera like Cephaliophora and Stephanosporaceae, which may play a role in organic matter decomposition. A null model analysis revealed that bacterial community assembly was largely shaped by deterministic processes, with stronger environmental selection pressures in PBAT-treated soils, while fungal communities were more influenced by stochastic processes. In addition, multiyear PBAT mulch application also impacted the functionality of the soil microbial communities. PBAT exposure enhanced biofilm formation in aerobic bacteria, promoting aerobic degradation processes while also reducing the abundance of stress-tolerant bacteria. Additionally, PBAT altered key microbial functions related to carbon, nitrogen, and sulfur cycling. Notably, the fungal communities exhibited functional shifts, with an increase in saprotrophic fungi being beneficial for nutrient cycling, alongside a potential rise in plant pathogenic fungi. These findings underscore the multiyear ecological impacts of biodegradable plastics, suggesting microbial adaptation to plastic degradation and changes in key ecological functions, with implications for agricultural sustainability and bioremediation strategies. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Rarefaction curves of bacterial (<b>a</b>) and fungal (<b>b</b>) communities based on observed OTUs of individual soil samples; boxplots of community α-diversity of bacteria (<b>c</b>) and fungi (<b>d</b>) (ns &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005, and **** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Genus-level species composition of bacteria (<b>a</b>) and fungi (<b>b</b>) in each soil sample, and effect of biodegradable mulching on species difference analysis of soil bacteria (<b>c</b>) and fungi (<b>d</b>) communities on genus level. Differences between groups were calculated using Wilcoxon rank-sum test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Venn diagrams showing the unique and shared bacterial (<b>a</b>) and fungal (<b>b</b>) OTUs in different groups; the composition of the shared 48 bacterial species (<b>c</b>) and 35 fungal species (<b>d</b>) in the TZ groups.</p>
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<p>The fraction of assembly mechanism of soil bacterial (<b>a</b>) and fungal (<b>b</b>) communities in different treated soils based on the β-Nearest Taxon Index (βNTI). HeS, heterogeneous selection; HoS, homogeneous selection; HD, homogenizing dispersal; DL, dispersal limitation; DR, drift and others.</p>
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<p>Phenotypic differences in colonies based on Bugbase prediction (ns &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01) (<b>a</b>) and prediction of fungal functions in different treated soils based on FUNGuild (<b>b</b>). Predicted relative abundance of bacteria in stress-tolerant (<b>c</b>) and bacteria containing mobile elements (<b>d</b>) based on BugBase database. For panel (<b>b</b>): AP, Animal Pathogen; DS, Dung Saprotroph; Ec, Ectomycorrhizal; En, Endophyte; Ep, Epiphyte; FP, Fungal Parasite; LP, Lichen Parasite; LS, Litter Saprotroph; PP, Plant Pathogen; PS, Plant Saprotroph; SS, Soil Saprotroph; US, Undefined Saprotroph; WS, Wood Saprotroph.</p>
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<p>Nutrient cycling-related functions of bacterial communities as predicted by FAPROTAX.</p>
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19 pages, 9146 KiB  
Article
Transcriptome Analysis of Magnolia sieboldii K. Koch in Response to Heat Stress
by Jinling Wang, Yaling Wang, Ruijian Wang, Jing Wang and Yongxiang Kang
Forests 2025, 16(2), 218; https://doi.org/10.3390/f16020218 - 24 Jan 2025
Viewed by 241
Abstract
Magnolia sieboldii K. Koch is a relict plant species that survived in the glacial period. The species possesses significant esthetic value and is predominantly found in vertically stratified high-altitude forests located in southern China. The primary limiting factor for urban greening when introducing [...] Read more.
Magnolia sieboldii K. Koch is a relict plant species that survived in the glacial period. The species possesses significant esthetic value and is predominantly found in vertically stratified high-altitude forests located in southern China. The primary limiting factor for urban greening when introducing high-altitude species to low-altitude areas is excessive temperature. However, the response mechanism of M. sieboldii to elevated temperatures remains unclear. In this study, we employed the RNASeq technique to investigate the response mechanism of M. sieboldii under heat stress conditions. A total of 88,746 unigenes were obtained, with over 36.51% of these unigenes being annotated in at least one publicly available database. The comparison of the 35 °C and 40 °C treatment groups with the control group revealed a total of 7470 and 13,494 differentially expressed genes (DEGs), respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the differentially up-regulated and down-regulated genes were implicated in plant–pathogen interactions, plant hormone signal transduction, and the MAPK signaling pathway-plant. Differential expression genes associated with the response to heat stress were also observed, including transcription factors such as AP2/EREBPs, WRKY, NACs, MYBs, bZIPs, and HSFs. These transcription factors may collectively modulate cellular metabolism, signal transduction pathways, and the synthesis as well as degradation of response proteins in M. sieboldii. In addition, network analysis using STRING on different genes revealed that the central node proteins in the network were CLPB1, HSP70-4, HOP3, P58IPK, HSP90-2, ERDJ3B, and MBF1C, all of which exhibited associations with heat tolerance. The findings of this study enhance our comprehension of the molecular regulatory mechanism underlying heat stress in M. sieboldii, which holds significant implications for investigating its translocation from high-altitude to low-altitude regions and ex situ conservation. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>Injury percentage of <span class="html-italic">Magnolia sieboldii</span> K. Koch cells under different temperatures.</p>
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<p>The length distribution of unigene of <span class="html-italic">M. sieboldii</span> transcriptome.</p>
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<p>Annotations statistics of unigene of <span class="html-italic">M. sieboldii</span> transcriptome.</p>
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<p>GO categorization functional annotation of the unigenes.</p>
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<p>KEGG categorization functional annotation of the unigenes.</p>
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<p>KEGG pathway classifications of the DEGs in T35 vs. T25.</p>
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<p>KEGG pathway classifications of the DEGs in T40 vs. T25.</p>
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<p>KEGG Pathway with DEGs in different groups.</p>
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<p>Transcript gene family classification of <span class="html-italic">M. sieboldii</span> transcriptome.</p>
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<p>The FPKM of DEGs in TFs analysis. Bars show the standard error of the relative expression levels. * represents that the FPKM in heat-stressed <span class="html-italic">M. sieboldii</span> is significantly different from the control (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Analysis of genes potentially involved in high temperature response following a STRING network analysis using a minimum required interaction score of 0.4; line thickness indicating the strength of data support. The node color correlates with the node degree; specifically, nodes with higher degrees are represented by darker colors.</p>
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<p>The FPKM of DEGs STRING analysis. Bars show the standard error of the relative expression levels. * represents that the FPKM in heat-stressed <span class="html-italic">M. sieboldii</span> is significantly different from the control (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>qRT-PCR analysis of TFs in response to different heat stresses. Three bio-replicates and tech-replicates were performed. Data are presented as means ± SD. Bars show the standard error of the relative expression levels. * represents that the relative expression level in heat-stressed <span class="html-italic">M. sieboldii</span> is significantly different from the control (<span class="html-italic">p</span> &lt; 0.05).</p>
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27 pages, 1663 KiB  
Review
Ligands for Intestinal Intraepithelial T Lymphocytes in Health and Disease
by Akanksha Hada and Zhengguo Xiao
Pathogens 2025, 14(2), 109; https://doi.org/10.3390/pathogens14020109 - 23 Jan 2025
Viewed by 407
Abstract
The intestinal tract is constantly exposed to a diverse mixture of luminal antigens, such as those derived from commensals, dietary substances, and potential pathogens. It also serves as a primary route of entry for pathogens. At the forefront of this intestinal defense is [...] Read more.
The intestinal tract is constantly exposed to a diverse mixture of luminal antigens, such as those derived from commensals, dietary substances, and potential pathogens. It also serves as a primary route of entry for pathogens. At the forefront of this intestinal defense is a single layer of epithelial cells that forms a critical barrier between the gastrointestinal (GI) lumen and the underlying host tissue. The intestinal intraepithelial T lymphocytes (T-IELs), one of the most abundant lymphocyte populations in the body, play a crucial role in actively surveilling and maintaining the integrity of this barrier by tolerating non-harmful factors such as commensal microbiota and dietary components, promoting epithelial turnover and renewal while also defending against pathogens. This immune balance is maintained through interactions between ligands in the GI microenvironment and receptors on T-IELs. This review provides a detailed examination of the ligands present in the intestinal epithelia and the corresponding receptors expressed on T-IELs, including T cell receptors (TCRs) and non-TCRs, as well as how these ligand-receptor interactions influence T-IEL functions under both steady-state and pathological conditions. By understanding these engagements, we aim to shed light on the mechanisms that govern T-IEL activities within the GI microenvironment. This knowledge may help in developing strategies to target GI ligands and modulate T-IEL receptor expression, offering precise approaches for treating intestinal disorders. Full article
(This article belongs to the Section Immunological Responses and Immune Defense Mechanisms)
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<p>Availability of potential ligands and the possible expression of T-IEL receptors in steady-state and disease conditions. This diagram illustrates the crucial roles of ligand availability and receptor expression on T-IELs within the intestinal epithelial environment under both steady-state (<b>left</b>) and disease (<b>right</b>) conditions. Under steady-state conditions, specific ligands and their corresponding receptors are essential for preserving T-IEL populations, preventing microbial invasion, maintaining immune tolerance, conducting immune surveillance, sustaining the integrity of the epithelial barrier, and communicating with the enteric nervous system, ultimately maintaining homeostasis. In contrast, during disease conditions, changes in ligand availability and ligand-receptor interactions aid in eliminating pathogens, stressed and malignantly transformed cells, as well as in promoting tissue repair and healing. TCR, T cell receptor; PRR, pattern recognition receptor; TLR, toll-like receptor; AhR, aryl hydrocarbon receptor; IL, interleukin; MHC, major histocompatibility complex; HVEM, herpesvirus entry mediator receptor; GLP, glucagon-like peptide; VIP, vasoactive intestinal peptide; eCIRP, extracellular cold-inducible RNA-binding protein; JAML, junctional adhesion molecule-like; CAR, coxsackievirus and adenovirus receptor. Created in BioRender.com (accessed on 15 January 2025).</p>
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15 pages, 3748 KiB  
Article
Molecular Insights into the Role of the MET30 Protein and Its WD40 Domain in Colletotrichum gloeosporioides Growth and Virulence
by Fei Wu, Qianlong Sun, Longhui Huang, Sizhen Liu, Yue Chen, Xin Zhang, Chenggang Li, Sheng Guo and Xinqiu Tan
J. Fungi 2025, 11(2), 84; https://doi.org/10.3390/jof11020084 - 21 Jan 2025
Viewed by 479
Abstract
Colletotrichum gloeosporioides is a major phytopathogen responsible for anthracnose in Capsicum annuum (pepper) which leads to significant yield losses. At present, the molecular mechanism of C. gloeosporioides pathogenesis is not very clear. In this study, we focused on the MET30 protein and its [...] Read more.
Colletotrichum gloeosporioides is a major phytopathogen responsible for anthracnose in Capsicum annuum (pepper) which leads to significant yield losses. At present, the molecular mechanism of C. gloeosporioides pathogenesis is not very clear. In this study, we focused on the MET30 protein and its key WD40 domain, with an emphasis on its role in the biological functions of C. gloeosporioides. Bioinformatics analysis revealed that the MET30 protein contains a conserved F-box domain and multiple WD40 repeats, which interact with other proteins to participate in various cellular processes, including nutrient acquisition, stress responses, and pathogenicity. Gene knockout and complementation experiments demonstrated that deleting the MET30 protein or its WD40 domain significantly reduced the rates of spore production and hyphal growth while increasing tolerance to environmental stresses such as high salinity and oxidative stress. Furthermore, pathogenicity assays revealed that the WD40 domain of the MET30 protein is crucial for regulating fungal pathogenicity, as mutants lacking WD40 domains presented increased virulence on pepper leaves. These findings suggest that the WD40 domain, in synergy with the MET30 protein, regulates the pathogenicity and stress response of C. gloeosporioides, provides new insights into the molecular mechanisms of anthracnose, and offers potential strategies for effective disease control. Full article
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<p>The CgMET30 protein structure was predicted and analysed. (<b>A</b>) The predicted structural domains of the CgMET30 protein. (<b>B</b>) The predicted tertiary structure of the CgMET30 protein. (<b>C</b>) The predicted tertiary structure of the CgMET30 protein.</p>
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<p>CgMET30 is involved in nutritional growth. (<b>A</b>,<b>B</b>) Growth rates on PDA, OM, SDC, and V8 media. (<b>C</b>,<b>D</b>) Comparison of conidia yields. (<b>E</b>) Expression levels of selected conidia-related genes detected via RT–qPCR. (<b>F</b>) Morphological observations of the formed appressoria. Observe using a 40x microscope. Scale bars represent 200 μm. Error bars represent ± SD of three replicates, asterisks (*) indicate significant difference (<span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.05), asterisks (**) indicate extremely significant difference (<span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Stress tolerance test. (<b>A</b>–<b>D</b>) Stress tolerance of all the strains to ions and salt. (<b>E</b>–<b>H</b>) Stress tolerance of all the strains to oxidative stress. Similar results were obtained after three repetitions of the procedure. Error bars represent ± SD of three replicates and asterisks (*) indicate significant difference (<span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.05), asterisks (**) indicate extremely significant difference (<span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>CgMET30 is involved in maintaining cell wall integrity. (<b>A</b>,<b>B</b>) Number of protoplasts released after mycelia were treated with equal amounts of lysing enzymes (scale bar, 200 μm). (<b>C</b>) RT–qPCR was used to measure the expression levels of cell wall synthase-related genes. Similar results were obtained after three repetitions of the procedure. Error bars represent ± SD of three replicates, asterisks (*) indicate significant difference (<span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.05),asterisks (**) indicate extremely significant difference (<span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Pathogenicity assay. (<b>A</b>,<b>B</b>) Inoculation of mycelia to assess pathogenicity. (<b>C</b>,<b>D</b>) Inoculation of conidia to assess pathogenicity. (<b>E</b>) Mycelium expansion was observed via Trypan blue staining. Similar results were obtained after three repetitions of the procedure. Error bars represent ± SD of three replicates and asterisks (**) indicate extremely significant difference. (<span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05).</p>
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13 pages, 3239 KiB  
Article
Gelatin Nanoemulsion-Based Co-Delivery of Terbinafine and Essential Oils for Treatment of Candida albicans Biofilms
by Muhammad Aamir Hassan, Sadaf Noor, Jungmi Park, Ahmed Nabawy, Maitri Dedhiya, Robin Patel and Vincent M. Rotello
Microorganisms 2025, 13(1), 127; https://doi.org/10.3390/microorganisms13010127 - 9 Jan 2025
Viewed by 654
Abstract
Fungal infections represent a significant global health challenge. Candida albicans is a particularly widespread pathogen, with both molecular and biofilm-based mechanisms making it resistant to or tolerant of available antifungal drugs. This study reports a combination therapy, active against C. albicans, utilizing [...] Read more.
Fungal infections represent a significant global health challenge. Candida albicans is a particularly widespread pathogen, with both molecular and biofilm-based mechanisms making it resistant to or tolerant of available antifungal drugs. This study reports a combination therapy, active against C. albicans, utilizing terbinafine and essential oils incorporated into a gelatin-based nanoemulsion system (T-GNE). Eugenol and methyl eugenol/terbinafine T-GNEs had an additive efficacy, while carvacrol (CT-GNE) worked synergistically with terbinafine, providing effective antifungal treatment with minimal mammalian cell toxicity. Confocal microscopy demonstrated that CT-GNE penetrated the dense C. albicans biofilm and disrupted the fungal cell membrane. Overall, the combination of essential oils with terbinafine in GNE provided a promising treatment for fungal biofilms. Full article
(This article belongs to the Section Biofilm)
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<p>(<b>a</b>) Schematic representation of terbinafine-essential oil-loaded gelatin nanoemulsion (T-GNE) fabrication through emulsification. (<b>b</b>) Representation of efficient biofilm penetration of T-GNEs, compared to poor penetration of essential oil and terbinafine alone due to their hydrophobicity. Characterization of T-GNEs via (<b>c</b>) DLS and (<b>d</b>) zeta potential.</p>
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<p>Checkerboard microdilution assay of essential oils combined with terbinafine into gelatin nanoemulsions (T-GNEs) against <span class="html-italic">C. albicans</span> IDRL-7034 based on minimal biofilm inhibitory concentration (MBIC). (<b>a</b>) A 2D checkerboard microdilution assay of carvacrol/eugenol/methyl eugenol-loaded gelatin nanoemulsions (C-GNEs) incorporation with terbinafine (16–128 mg/L). (<b>b</b>) Fractionation inhibitory concentration (FIC) indices correspond to synergy (≤0.5) for CT-GNE and exhibit additive effect (0.5 &lt; FIC Index ≤ 1) for ET-GNE and MT-GNE. (<b>c</b>) Comparison between the growth curve of GNEs and T-GNEs based on MBIC values.</p>
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<p>Comparison of the antibiofilm activity of terbinafine-essential oil-loaded gelatin nanoemulsions (T-GNEs) and essential oil-loaded gelatin nanoemulsions (GNEs) alone against 2-day-old <span class="html-italic">Candida albicans</span> IDRL-7034 biofilm showing effective antifungal activity, (<b>a</b>) antibiofilm efficacy of CT-GNE and C-GNE (<b>b</b>) ET-GNE and E-GNE, and (<b>c</b>) MT-GNE and M-GNE. Values are expressed as mean ± standard deviation of ≥3 replicates.</p>
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<p>Confocal laser scanning microscopy (CLSM) images of 2-day-old <span class="html-italic">Candida albicans</span> IDRL-7034 biofilm stained by PI (Red) and SYTO 9 (Green) (<b>a</b>) treated with CT-GNE, showing complete co-distribution of fluorescence and hence, complete biofilm penetration, (<b>b</b>) untreated as representative 3D views.</p>
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<p>(<b>a</b>) Viability of 3T3/NIH fibroblast cells (ATCC CRL-1658) after 3 h exposure to T-GNEs. (<b>b</b>) Resistance development against T-GNEs and terbinafine, showing fold changes in MICs across successive passages. Significantly, no resistance development was observed with any of the GNE systems.</p>
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