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Search Results (529)

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Keywords = Fusarium graminearum

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20 pages, 6371 KiB  
Article
Identification and Expression Analysis of Wheat Golden2-like (TaGLK) Gene in Response to Biotic and Abiotic Stress
by Junhui Xiang, Pingu Liu, Daniel Bimpong, Jiayi Shen, Xusi Liu, Siting Wang, Yan Li, Youning Wang and Dongfang Ma
Agronomy 2024, 14(12), 3070; https://doi.org/10.3390/agronomy14123070 - 23 Dec 2024
Abstract
The Golden2-like (GLK) transcription factors belong to the GARP family of transcription factors and play significant roles in plant growth, development, and responses to both abiotic and biotic stresses. This study employed bioinformatics and expression analyses to investigate the regulatory roles of wheat [...] Read more.
The Golden2-like (GLK) transcription factors belong to the GARP family of transcription factors and play significant roles in plant growth, development, and responses to both abiotic and biotic stresses. This study employed bioinformatics and expression analyses to investigate the regulatory roles of wheat GLK proteins under various stress conditions, including abscisic acid (ABA) treatment, osmotic stress, and infection by Fusarium graminearum. The study identified 125 TaGLK proteins and revealed that TaGLKs play a significant role in wheat’s development and response to adverse environmental conditions. The results indicate that TaGLKs may serve as potential transcriptional regulators capable of integrating multiple cellular signals to coordinate various developmental and physiological processes. Evolutionary analysis classified the TaGLK proteins into six subgroups, which shared similar conserved domains and motifs. Protein–protein interaction network analysis revealed that TaGLKs are involved in photoreceptor activity, cell cycle progression, and protein regulation. Gene expression analysis of TaGLKs discovered that they play key functions in wheat development, as well as regulation of biotic and abiotic stress conditions. RT-qPCR analysis showed that TaGLKs regulate earlier and late effects of osmotic stress, F. graminearum infections, and ABA treatment in wheat. These findings provide knowledge for future studies of the functions of TaGLK TFs in wheat stress tolerance and development, which could have significant implications for enhancing wheat tolerance to various environmental stressors. Full article
(This article belongs to the Special Issue Mechanism and Sustainable Control of Crop Diseases)
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Figure 1

Figure 1
<p>Phylogenetic tree of <span class="html-italic">GLK</span> proteins in wheat and other species. The nodes were tested through bootstrap analysis with 1000 replicates, the Poisson model, uniform rates, and pairwise deletion. The phylogenetic relationships of the <span class="html-italic">TaGLKs</span>, <span class="html-italic">AtGLKs</span>, and <span class="html-italic">OsGLKs</span> were classified into 6 subgroups (I, II, III, IV, V, and VI).</p>
Full article ">Figure 2
<p>Distribution of <span class="html-italic">TaGLKs</span> on chromosomes. The names of each chromosome (1A to Un) are shown in color blue at the left position. The <span class="html-italic">TaGLK</span> names are indicated at the right position of each chromosome with color red. The chromosome lengths are shown in Mb (millions of bases).</p>
Full article ">Figure 3
<p>Conserved motifs and gene structure analysis of <span class="html-italic">TaGLKs</span>. (<b>A</b>) Conserved motifs of <span class="html-italic">TaGLKs</span>. (<b>B</b>) Structural dynamics of <span class="html-italic">TaGLKs</span>: the color yellow represents CDSs, the color green indicates UTRs, and black lines represent introns.</p>
Full article ">Figure 4
<p><span class="html-italic">Cis</span>-acting element analyses of a 1 kb promoter region of the <span class="html-italic">TaGLKs</span>.</p>
Full article ">Figure 5
<p>GO annotation and protein interaction network of <span class="html-italic">TaGLKs</span>. (<b>A</b>) GO-term functional enrichment terms of <span class="html-italic">TaGLKs</span>. (<b>B</b>) GO annotation functions of <span class="html-italic">TaGLK</span> proteins. (<b>C</b>) Protein–protein interaction network of <span class="html-italic">TaGLK</span> proteins: Blue-colored <span class="html-italic">TaGLKs</span> are related to the Myb-like DNA-binding domain and CCT motif; red-colored <span class="html-italic">TaGLKs</span> are related to the detection of light stimulus and photoreceptor activity.</p>
Full article ">Figure 6
<p>The expression of <span class="html-italic">TaGLKs</span> under different tissue development, biotic, and abiotic stress conditions. (<b>A</b>) The expression levels of <span class="html-italic">TaGLKs</span> in various tissue development stages. (<b>B</b>) Expression of <span class="html-italic">TaGLKs</span> under different abiotic stresses. (<b>C</b>) Expression of <span class="html-italic">TaGLKs</span> under different biotic stresses.</p>
Full article ">Figure 7
<p>Expression levels of <span class="html-italic">TaGLKs</span> under drought stress. Significant differences between three biological replicates are denoted by asterisks, indicating statistical difference at <span class="html-italic">p</span> &lt; 0.05. The levels of significance are indicated as <span class="html-italic">p</span> &lt; 0.0001 (****), <span class="html-italic">p</span> &lt; 0.0005 (***), <span class="html-italic">p</span> &lt; 0.001 (**), and non-significant (ns) between treatment and control groups.</p>
Full article ">Figure 8
<p>Expression levels of <span class="html-italic">TaGLKs</span> under abscisic acid (ABA) treatment. Significant differences between three biological replicates are denoted by asterisks, indicating statistical difference at <span class="html-italic">p</span> &lt; 0.05. The levels of significance are indicated as <span class="html-italic">p</span> &lt; 0.0001 (****), <span class="html-italic">p</span> &lt; 0.001 (**), <span class="html-italic">p</span> &lt; 0.05 (*), and non-significant (ns) between treatment and control groups.</p>
Full article ">Figure 9
<p>Expression levels of <span class="html-italic">TaGLKs</span> under <span class="html-italic">Fusarium graminearum</span> infection. Significant differences between three biological replicates are denoted by asterisks, indicating statistical difference at <span class="html-italic">p</span> &lt; 0.05. The levels of significance are indicated as <span class="html-italic">p</span> &lt; 0.0001 (****), <span class="html-italic">p</span> &lt; 0.0005 (***), <span class="html-italic">p</span> &lt; 0.05 (*), and non-significant (ns) between treatment and control groups.</p>
Full article ">
19 pages, 1782 KiB  
Article
Effects of a Microbial Vetch Fertilizer on the Disease Resistance, Yield, and Quality of Sweet Waxy Corn
by Xiangtao Meng, Zhuangzhuang Li, Han Wu, Haiming Duan, Li Yu, Cheng Zhou, Meng Wang, Kun Zhang, Chaofan Hu, Zhangjun Su and Haibing Yu
Diversity 2024, 16(12), 778; https://doi.org/10.3390/d16120778 - 22 Dec 2024
Viewed by 283
Abstract
This study aimed to address stalk rot in sweet waxy corn while simultaneously decreasing the chemical fertilizer usage without affecting the crop yield. The investigators implemented an innovative approach that integrated disease management with environmentally sustainable agricultural practices by developing an enhanced microbial [...] Read more.
This study aimed to address stalk rot in sweet waxy corn while simultaneously decreasing the chemical fertilizer usage without affecting the crop yield. The investigators implemented an innovative approach that integrated disease management with environmentally sustainable agricultural practices by developing an enhanced microbial vetch fertilizer (MVF). This novel fertilizer was produced through the fermentation of vetch (Vicia villosa var. glabrescens) straw utilizing beneficial strains of Trichoderma and Bacillus species. In vitro experiments demonstrated that the antifungal microbial strains effectively inhibited Fusarium graminearum growth by 46.9% to 64.0%. Subsequent pot trials revealed that MVF application significantly reduced the incidence of stalk rot, resulting in a disease index of 21.2, which was equivalent to control efficacy of 60.2%. Field experiments further demonstrated that applying MVF at 5250 kg·ha−1 produced optimal ear and grain weights, with the peak grain yield reaching 11,259.7 kg·ha−1 when combined with 90% of the standard chemical fertilizer regime. This study contributes to the advancement of environmentally sustainable agricultural practices by effectively managing corn stalk rot and improving productivity by using eco-friendly techniques. The MVF shows potential as a biological alternative to boost sweet corn yields and enhance the protective enzyme activity. This study advances the field of sustainable agriculture by introducing eco-friendly techniques that effectively combat corn stalk rot and enhance crop yields. Full article
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<p>Effects of confrontation cultures of two <span class="html-italic">Trichoderma</span> and three <span class="html-italic">Bacillus</span> strains on corn stalk rot <span class="html-italic">F. graminearum</span>. (<b>a</b>) Confrontation culture of <span class="html-italic">T. harzianum</span> dhm4 with the pathogen for 96 h. (<b>b</b>) Confrontation culture of <span class="html-italic">T. asperellum</span> dhm5 with the pathogen for 96 h. (<b>c</b>) Confrontation culture of <span class="html-italic">B. amyloliquefaciens</span> dhm1 with the pathogen for 120 h. (<b>d</b>) Confrontation culture of <span class="html-italic">B. velezensis</span> dhm2 with the pathogen for 120 h. (<b>e</b>) Confrontation culture of <span class="html-italic">B. subtilis</span> dhm3 with the pathogen for 120 h. (<b>f</b>) Control.</p>
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<p>Effects of MVF formulated with different ratios of mixed <span class="html-italic">Bacillus</span> fermentation broth on disease index and effectiveness in controlling corn stalk rot were examined. Statistical equivalence is denoted by identical letters, whereas significant disparities at the 5% probability level are indicated by different letters, as established through Duncan’s multiple range test (DMRT). Error bars represent the standard error of the mean.</p>
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<p>Effects of variations in MVF amount on the growth characteristics of sweet waxy corn FengNuo 168. Treatments T1–T5 corresponded to MVF application rates of 2625, 5250, 10,500, 15,750, and 21,000 kg·ha<sup>−1</sup>, respectively. Significant differences among treatments at the 5% probability level are indicated by distinct lowercase letters, as determined by the DMRT. Error bars represent the standard error of the mean.</p>
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<p>Influence of MVF and chemical fertilizer combination on corn growth metrics. Distinct lowercase letters denote statistically significant variations among treatments at <span class="html-italic">p</span> &lt; 0.05, as established by the DMRT. Error bars represent the standard error of the mean.</p>
Full article ">Figure 5
<p>Effects of combining MVF and chemical fertilizer on SPAD value in ear leaves at the harvest stage. Different lowercase letters indicate significant differences among treatments at the 5% level of probability, as determined by the DMRT. Error bars represent the standard error of the mean.</p>
Full article ">Figure 6
<p>Effects of MVF and chemical fertilizer co-application on SOD, POD, and CAT enzyme activity in sweet waxy corn leaf tissue. Statistically significant variations between treatments are denoted by distinct lowercase letters, as determined by the DMRT at a 5% significance level. Error bars represent the standard error of the mean.</p>
Full article ">Figure 7
<p>Effects of MVF combined with chemical fertilizer on the composition of sweet waxy corn kernels were examined, with a focus on protein content, reducing sugar levels, and amino acid profiles. Statistical significance between different treatments (<span class="html-italic">p</span> &lt; 0.05) is indicated by unique lowercase letters, as determined by the DMRT. Error bars represent the standard error of the mean.</p>
Full article ">
16 pages, 6782 KiB  
Article
Functional Characterization of FgAsp, a Gene Coding an Aspartic Acid Protease in Fusarium graminearum
by Ping Li, Zhizhen Fu, Mengru Wang, Tian Yang, Yan Li and Dongfang Ma
J. Fungi 2024, 10(12), 879; https://doi.org/10.3390/jof10120879 - 17 Dec 2024
Viewed by 342
Abstract
Aspartic proteases (APs), hydrolases with aspartic acid residues as catalytic active sites, are closely associated with processes such as plant growth and development and fungal and bacterial pathogenesis. F. graminearum is the dominant pathogenic fungus that causes Fusarium head blight (FHB) in wheat. [...] Read more.
Aspartic proteases (APs), hydrolases with aspartic acid residues as catalytic active sites, are closely associated with processes such as plant growth and development and fungal and bacterial pathogenesis. F. graminearum is the dominant pathogenic fungus that causes Fusarium head blight (FHB) in wheat. However, the relationship of APs to the growth, development, and pathogenesis of F. graminearum is not clear. Therefore, we selected the FGSG_09558 gene, whose function annotation is aspartate protease, for further study. In this study, FGSG_09558 was found to contain a conserved structural domain and signal peptide sequence of aspartic acid protease and was therefore named FgAsp. The function of FgAsp in F. graminearum was investigated by constructing the knockout and complementation mutants of this gene. The results showed that with respect to the wild type (PH-1), the knockout mutant showed a significant reduction in mycelial growth, asexual spore production, and sexual spore formation, highlighting the key role of FgAsp in the growth and development of F. graminearum. In addition, the mutants showed a significant reduction in the virulence and accumulation level of deoxynivalenol (DON) content on maize whiskers, wheat germ sheaths, and wheat ears. DON, as a key factor of virulence, plays an important role in the F. graminearum infection of wheat ears, suggesting that FgAsp is involved in the regulation of F. graminearum pathogenicity by affecting the accumulation of the DON toxin. FgAsp had a significant effect on the ability of F. graminearum to utilize various sugars, especially arabinose. In response to the stress, hydrogen peroxide inhibited the growth of the mutant most significantly, indicating the important function of FgAsp in the strain’s response to environmental stress. Finally, FgAsp plays a key role in the regulation of F. graminearum growth and development, pathogenicity, and environmental stress response. Full article
(This article belongs to the Special Issue Growth and Virulence of Plant Pathogenic Fungi)
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Figure 1
<p>The <span class="html-italic">FgAsp</span> gene deletion and complementation strategies.</p>
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<p>Description of <span class="html-italic">FgAsp</span>. (<b>A</b>) Conserved functional domain. (<b>B</b>) Identification of transmembrane domains. (<b>C</b>) Three-dimensional homology modeling. (<b>D</b>) Signal peptide prediction results. (<b>E</b>) Gene expression level of <span class="html-italic">FgAsp</span> in <span class="html-italic">F. graminearum</span>.</p>
Full article ">Figure 3
<p>(<b>A</b>) Colony morphology of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. (<b>B</b>) Growth rates of wild-type PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> strains. (<b>C</b>) PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> hyphal edge morphology. Scale bar = 20 μm. Means and standard errors were calculated using <span class="html-italic">t</span>-tests based on data from three independent biological replicates. Different letters indicate significant difference at the level of 0.05.</p>
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<p>(<b>A</b>) Conidiophores of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. The red arrows indicate the attached conidia on the conidial peduncle of each strain. Scale bar = 25 μm. (<b>B</b>) The sporogenesis rates of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. Different lowercase letters a and b represent significant differences. (<b>C</b>) Statistics of the number of septa in conidia of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. Scale bar = 25 μm. (<b>D</b>) Conidia germination statistics of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>.</p>
Full article ">Figure 5
<p>Pathogenicity and lesion length of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>: (<b>A1</b>,<b>A2</b>) Wheat coleoptiles, (<b>B1</b>,<b>B2</b>) wheat leaves, (<b>C1</b>,<b>C2</b>) wheat ears, (<b>D1</b>,<b>D2</b>) corn silks. The images above show the pathogenicity and lesion pictures, and the violin plot of lesion length is displayed below.</p>
Full article ">Figure 6
<p>Sexual reproduction of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>: (<b>A</b>) Number of ascospores produced by sexual reproduction of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. (<b>B</b>) Eruption of ascocarp primordia. Scale bar = 2000 μm. (<b>C</b>) Ascospores. Scale bar = 50 μm. (<b>D</b>) Number of ascospores per asci (individuals). Different lowercase letters a and b represent significant differences.</p>
Full article ">Figure 7
<p>(<b>A</b>) DON toxin content in TBI medium of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. (<b>B</b>) DON toxin content in wheat kernels of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span>. (<b>C</b>) Expression levels of TRI gene clusters in PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> after 6 days of TBI culture. Means and standard errors were calculated using <span class="html-italic">t</span>-tests based on data from three independent biological replicates. Different letters indicate significant difference at the level of 0.05.</p>
Full article ">Figure 8
<p>(<b>A</b>) Colony morphology of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> on PSA medium containing NaCl, KCl, MgCl<sub>2</sub>, CaCl<sub>2</sub>, and H<sub>2</sub>O<sub>2</sub>. (<b>B</b>) Stress growth inhibition rate analysis. Means and standard errors were calculated using <span class="html-italic">t</span>-tests based on data from three independent biological replicates. An asterisk (*) indicates a <span class="html-italic">p</span> value of less than 0.05, that is, the difference is significant at the 5% significance level. Two asterisks (**) indicate a <span class="html-italic">p</span> value of less than 0.01, that is, significant at the 1% significance level. Three asterisks (***) indicate a <span class="html-italic">p</span> value of less than 0.001, which is extremely significant at the 0.1% significance level. ns indicates no difference.</p>
Full article ">Figure 9
<p>(<b>A</b>) Colony morphology of PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> on PSA medium containing sucrose, arabinose, mannose, glucose, and galactose. (<b>B</b>) Analysis of different glycogen inhibition rates of wild-type PH-1, Δ<span class="html-italic">FgAsp</span>, and CΔ<span class="html-italic">FgAsp</span> strains. Data were tested by <span class="html-italic">t</span>-test, and error bars represent the standard deviation (SD). Different letters indicate a significant difference at the level of 0.05.</p>
Full article ">
12 pages, 1032 KiB  
Article
Rapid In-Field Detection of Airborne Pathogens Using Loop-Mediated Isothermal Amplification (LAMP)
by Alessia Bani, Corinne Whitby, Ian Colbeck, Alex J. Dumbrell and Robert M. W. Ferguson
Microorganisms 2024, 12(12), 2578; https://doi.org/10.3390/microorganisms12122578 - 13 Dec 2024
Viewed by 503
Abstract
Multiple human and plant pathogens are dispersed and transmitted as bioaerosols (e.g., Mycobacterium tuberculosis, SARS-CoV-2, Legionella pneumophila, Aspergillus fumigatus, Phytophthora spp., and Fusarium graminearum). Rapid, on-site methods to detect airborne pathogens would greatly enhance our ability to monitor exposure [...] Read more.
Multiple human and plant pathogens are dispersed and transmitted as bioaerosols (e.g., Mycobacterium tuberculosis, SARS-CoV-2, Legionella pneumophila, Aspergillus fumigatus, Phytophthora spp., and Fusarium graminearum). Rapid, on-site methods to detect airborne pathogens would greatly enhance our ability to monitor exposure and trigger early mitigation measures across different settings. Analysis of air samples for microorganisms in a regulatory context is often based on culture-based methods, which are slow, lack specificity, and are not suitable for detecting viruses. Molecular methods (based on nucleic acids) could overcome these challenges. For example, loop-mediated isothermal amplification (LAMP) is rapid, sensitive, specific, and may detect microbial pathogens from air samples in under 60 min. However, the low biomass in air samples makes recovering sufficient nucleic acids for detection challenging. To overcome this, we present a simple method for concentrating bioaerosols collected through liquid impingement (one of the most common methods for bioaerosol collection). This method paired with LAMP (or other molecular approaches) offers simple, rapid, and sensitive detection of pathogens. We validated this method using three airborne pathogens (Mycobacterium tuberculosis, Legionella pneumophila, and Aspergillus fumigatus), and we were able to detect fewer than five cells in a 15 mL liquid impinger air sample in under 60 min. This simple method offers rapid pathogen detection without the use of specialist equipment, and it can be used across healthcare, education, environmental monitoring, and military settings. Full article
(This article belongs to the Special Issue Detection and Identification of Pathogenic Bacteria and Viruses)
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Figure 1
<p>Workflow for rapid detection of airborne pathogens, from the air sample to the result in under 60 min.</p>
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<p>(<b>A</b>) Gel electrophoresis image of LAMP products and (<b>B</b>) reaction tubes at 0, 30, and 50 min for LAMP assay for the <span class="html-italic">E. coli malB</span> gene. Yellow color indicates positive LAMP reaction, orange indicates negative reaction. L = ladder (1 KB); the number indicates the number of cells in the reaction. <span class="html-italic">Rhodococcus</span> sp. negative control contained (10<sup>4</sup> cells reaction<sup>−1</sup>). NTC = No template control (i.e., PCR-grade water in place of DNA template/cells).</p>
Full article ">
19 pages, 5894 KiB  
Article
Application of Pulsed Electric Field During Malting: Impact on Fusarium Species Growth and Mycotoxin Production
by Nela Prusova, Marcel Karabin, Lukas Jelinek, Jana Chrpova, Jaroslava Ovesna, Pavel Svoboda, Tereza Dolezalova, Adam Behner, Jana Hajslova and Milena Stranska
Toxins 2024, 16(12), 537; https://doi.org/10.3390/toxins16120537 - 12 Dec 2024
Viewed by 478
Abstract
The increasing contamination of cereals by micromycetes and mycotoxins during malting still poses an unresolved food safety problem. This study characterises the potential of the novel, rapidly developing food production technology of Pulsed Electric Field (PEF) to reduce the viability of Fusarium fungi [...] Read more.
The increasing contamination of cereals by micromycetes and mycotoxins during malting still poses an unresolved food safety problem. This study characterises the potential of the novel, rapidly developing food production technology of Pulsed Electric Field (PEF) to reduce the viability of Fusarium fungi and the production of mycotoxins during malting. Barley, artificially inoculated with four Fusarium species, was treated by PEF with two different intensities and then malted using a standard Pilsner-type technology. Concentrations of fungi were quantified by RT-PCR, expression of fungal growth-related genes was assessed using mRNA sequencing, and mycotoxin levels were analysed by U-HPLC-HRMS/MS. Despite the different trends for micromycetes and mycotoxins after application of variously intense PEF conditions, significant reductions were generally observed. The greatest decrease was for F. sporotrichioides and F. poae, where up to six fold lower levels were achieved for malts produced from the PEF-treated barley when compared to the control. For F. culmorum and F. graminearum, up to a two-fold reduction in the PEF-generated malts was observed. These reductions mostly correlated with a decrease in relevant mycotoxins, specifically type A trichothecenes. Full article
(This article belongs to the Section Mycotoxins)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Levels of <span class="html-italic">Fusarium</span> species (<b>A</b>) and relevant mycotoxins (<b>B</b>) in final malts produced from pre-soaked barley without PEF treatment (control) and pre-soaked barley treated by PEF (<span class="html-italic">experiment I</span>). Error bars represent the variability between individual averaged samples; <span class="html-italic">n</span> = 9. Data were statistically processed using a two-sample <span class="html-italic">t</span>-test with unequal variance; statistical differences (<span class="html-italic">p</span>-value &lt; 0.05) are indicated by letters. If the result was not statistically significantly different between the control and PEF-supported sample, both columns are marked with the letter ‘a’. In the case of a statistically significant difference, the PEF-supported sample is marked with the letter ‘b’.</p>
Full article ">Figure 2
<p>Levels of <span class="html-italic">Fusarium</span> species (<b>A</b>) and relevant mycotoxins (<b>B</b>) in final malts produced from pre-soaked barley without PEF treatment (control) and pre-soaked barley treated by PEF (<span class="html-italic">experiment II</span>). Error bars represent the variability between individual averaged samples; <span class="html-italic">n</span> = 9. Data were statistically processed using a two-sample <span class="html-italic">t</span>-test with unequal variance; statistical differences (<span class="html-italic">p</span>-value &lt; 0.05) are indicated by letters. If the result was not statistically significantly different between the control and PEF-supported sample, both columns are marked with the letter ‘a’. In the case of a statistically significant difference, the PEF-supported sample is marked with the letter ‘b’.</p>
Full article ">Figure 3
<p>Transfer of micromycetes in the dry matter of intermediates during the production of malt from pre-soaked barley without PEF treatment (control) and pre-soaked barley treated with PEF (<span class="html-italic">experiment II</span>). The total amount of each micromycete in the input barley = 100%. Error bars express the variability between individual averaged samples (<span class="html-italic">n</span> = 9). Data were statistically processed using a two-sample <span class="html-italic">t</span>-test with unequal variance; statistical differences (<span class="html-italic">p</span>-value &lt; 0.05) are indicated by letters. If the result was not statistically significantly different between the control and PEF-supported sample, both columns are marked with the letter ‘a’. In the case of a statistically significant difference, the PEF-supported sample is marked with the letter ‘b’. The results for the ‘Green malt I’ intermediates were not included due to visible mould contamination and outlying values in these samples.</p>
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<p>Transfer of mycotoxins produced by <span class="html-italic">Fusarium</span> species used for barley inoculation into the dry matter of intermediates during the production of malt from pre-soaked barley without PEF treatment (control) and pre-soaked barley treated with PEF (during <span class="html-italic">experiment II</span>). The total amount of each mycotoxin in the input barley = 100%. Error bars express the variability between individual averaged samples (<span class="html-italic">n</span> = 9). Data were statistically processed using the two-sample <span class="html-italic">t</span>-test with unequal variance; the statistical differences (<span class="html-italic">p</span>-value &lt; 0.05) are indicated by letters. If the result was not statistically significantly different between the control and PEF-supported sample, both columns are marked with the letter ‘a’. In the case of a statistically significant difference, the PEF-supported sample is marked with the letter ‘b’.</p>
Full article ">Figure 5
<p>Timeline of malting experiment after the <span class="html-italic">experiment II</span> conditions. Malting intermediates analysed are shown in boxes.</p>
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23 pages, 6131 KiB  
Article
Salicylic Acid Mediates Chitosan-Induced Immune Responses and Growth Enhancement in Barley
by Pawel Poznanski, Abdullah Shalmani, Marcin Bryla and Waclaw Orczyk
Int. J. Mol. Sci. 2024, 25(24), 13244; https://doi.org/10.3390/ijms252413244 - 10 Dec 2024
Viewed by 584
Abstract
Chitosan (CS), derived from the partial deacetylation and hydrolysis of chitin, varies in the degree of deacetylation, molecular weight, and origin, influencing its biological effects, including antifungal properties. In plants, CS triggers immune responses and stimulates biomass growth. Previously, we found that the [...] Read more.
Chitosan (CS), derived from the partial deacetylation and hydrolysis of chitin, varies in the degree of deacetylation, molecular weight, and origin, influencing its biological effects, including antifungal properties. In plants, CS triggers immune responses and stimulates biomass growth. Previously, we found that the antifungal activity of CS was strongly dependent on its physicochemical properties. This study revealed that the chitosan batch CS_10 with the strongest antifungal activity also effectively activated plant immune responses and promoted biomass growth. Barley treated with CS_10 exhibited systemic acquired resistance (SAR), characterized by micronecrotic reactions upon Puccinia hordei (Ph) inoculation and reduced symptoms following Fusarium graminearum (Fg) infection, representing biotrophic and necrotrophic pathogens, respectively. CS_10 treatment (concentration 200 ppm) also enhanced plant biomass growth (by 11% to 15%) and promoted the accumulation of salicylic acid (SA), a hormone that regulates both plant immune responses and growth. Low levels of exogenous SA applied to plants mirrored the stimulation observed with CS_10 treatment, suggesting SA as a key regulator of CS_10-induced responses. Transcriptomic analysis identified SA-regulated genes as drivers of enhanced immunity and biomass stimulation. Thus, CS_10 not only fortifies plant defenses against pathogens like Ph and Fg but also boosts growth through SA-dependent pathways. Full article
(This article belongs to the Special Issue The Chitosan Biomaterials: Advances and Challenges—2nd Edition)
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Figure 1
<p>Representative picture of infection symptoms on the third barley leaves inoculated with <span class="html-italic">Fusarium graminearum</span> (<span class="html-italic">Fg</span>) in plants where the second leaves were mock (<span class="html-italic">Hv</span>-mock)- or chitosan_10 (CS)-treated (<span class="html-italic">Hv</span>_CS) (<b>A</b>). Relative number of <span class="html-italic">Fg TRI4</span> gene copies (<span class="html-italic">Fg_TRI5</span>) per one copy of barley <span class="html-italic">EFG1</span> gene (<span class="html-italic">Hv_EFG1</span>) is shown. The results are from five independent biological repetitions and the average values of genes’ quantification are shown (<b>B</b>). Asterisks indicate significance level (based on one-way ANOVA and Tukey’s post hoc test) ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Plant–pathogen interaction of barley plants treated with mock (<span class="html-italic">Hv</span>-mock) or with chitosan_10 solution (<span class="html-italic">Hv</span>-CS) followed by inoculation with <span class="html-italic">Puccinia hordei</span> (<span class="html-italic">Ph</span>) urediniospores. The CS_10 or mock treatments were applied to the second leaves of the plants, and the third leaves of the same plants were inoculated with <span class="html-italic">Ph</span> urediniospores. This approach allowed us to detect the results of plant immune response induced by the CS-10 and not a direct inhibitory effect of the CS-10 on the pathogen. Representative pictures of infection symptoms on the third leaves of mock- and CS_10-treated plants scored six days post-inoculation (<b>A</b>). Representative pictures of microscopic observation of infection sites of calcofluor white stained leaf samples scored from 1 to 5 days post-inoculation. Scale bars = 100 µm (<b>B</b>). Representative pictures of leaf samples stained with DAB. Scale bars = 100 µm (<b>C</b>). The rates of micronecrotic reactions in <span class="html-italic">Ph</span> infection units on barley leaves. The mean values and standard deviation were calculated based on scoring one entire leaf from each time point and three biological replicates (<b>D</b>).</p>
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<p>Concentration of total salicylic acid (SA) in barley leaves collected one day (1 d) and three days (3 d) after mock (<span class="html-italic">Hv</span>-mock) or chitosan_10 treatment (<span class="html-italic">Hv</span>-CS), or inoculation with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>). Asterisks indicate significance level (based on one-way ANOVA and LSD post hoc test) * <span class="html-italic">p</span> ≤ 0.05 and ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Relative biomass gain of barley seedlings after 19 days of cultivation in Hoagland medium after treatment with seven chitosan batches (200 ppm): CS_5, CS_8-15, CS_10, CS_10-120, CS_30-100, CS_100-300, and CS_300-1000. For each sample, 40 separate plants have been tested (<b>A</b>). Relative biomass gain of barley seedlings after 19 days of cultivation in Hoagland medium after chitosan (CS_10, 200 ppm) and after salicylic acid (SA, 50 μM and 400 μM) treatment (<b>B</b>). Each box represents the percentile in range 25–75; the whiskers represent the 10 and 90 percentiles. Asterisks indicate significance level (based on one-way ANOVA and Tukey’s post hoc test) * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Representative picture of leaf samples used for RNA-seq analysis. <span class="html-italic">Hv</span>_mock—leaves treated with mock solution containing 0.05% acetic acid; <span class="html-italic">Hv</span>_CS—leaves treated with CS_10 (solutions of CS_10 also contained 0.05% acetic acid); <span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>—leaves inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>); <span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS—leaves inoculated with <span class="html-italic">Fg</span> and treated with CS.</p>
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<p>Hierarchical clustering heatmap of tested variants: leaf control samples (<span class="html-italic">Hv</span>_mock), leaves treated with CS_10 (<span class="html-italic">Hv</span>_CS), inoculated with <span class="html-italic">F.</span> graminearum (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>), and treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS). The three columns in each variant represent the three biological replicates (<b>A</b>). Correlation matrix of all three biological replicates of each tested variant (<b>B</b>). Principal component analysis of all tested variants (<b>C</b>).</p>
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<p>Numbers of differentially expressed genes (DEGs) in analyzed samples in relation to the control (<span class="html-italic">Hv</span>_mock). The tested variants include leaves treated with CS_10 (<span class="html-italic">Hv</span>-CS), leaves inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>) (<span class="html-italic">Hv</span>-<span class="html-italic">Fg</span>), and leaves treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>_CS) (<b>A</b>). Venn diagrams showing number of differentially expressed genes (DEGs) in each the three tested variants in relation to mock-treated control samples. Variants: leaves treated with CS_10 (<span class="html-italic">Hv</span>-CS), leaves inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv</span>-<span class="html-italic">Fg</span>), and leaves treated with CS_10 and inoculated with <span class="html-italic">Fg</span> (<span class="html-italic">Hv_Fg</span>_CS) (<b>B</b>). Presented genes are based on a cutoff value of FDR &lt; 0.05 and log2fold change &gt; 2.</p>
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<p>Top five Gene Ontology terms sorted by fold enrichment across chitosan_10 treated barley (<span class="html-italic">Hv</span>_CS) and <span class="html-italic">F. graminearum</span> inoculated barley (<span class="html-italic">Hv</span>_<span class="html-italic">Fg</span>) categorized into BPs (biological processes), MF (molecular function) and CC (cellular component) gene sets.</p>
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<p>Regulation pattern of PAL- and ICS-encoding genes in variants of chitosan_10-treated (<span class="html-italic">Hv</span>_CS), <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>)-inoculated (<span class="html-italic">Hv_Fg</span>), and CS_10-treated and <span class="html-italic">Fg</span>-inoculated barley.</p>
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<p>Regulation pattern of genes encoding NPR1, NPR3, and NPR4 regulators, selected WRKY transcription factors and pathogenesis-related (PR) proteins in variants of chitosan_10-treated (<span class="html-italic">Hv</span>_CS), <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>)-inoculated (<span class="html-italic">Hv_Fg</span>), and chitosan_10-treated and <span class="html-italic">Fg</span>-inoculated barley plants. The blue color indicates the SA-related genes and pathways.</p>
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<p>Validation of RNA-seq differentially expressed genes (DEGs) using RT-qPCR of four genes <span class="html-italic">NPR1</span>, <span class="html-italic">PR9</span>, <span class="html-italic">PR4</span>, and <span class="html-italic">PR14</span>. The log2-fold change values (<b>A</b>) and the linear regression between the log2-fold change of RNA-seq and RT-qPCR quantification are shown. The points represent individual results for each gene and the three variants (<b>B</b>).</p>
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<p>Schematic representation of experimental variants for transcriptome sequencing. Briefly, 14-day-old plants were inoculated with <span class="html-italic">F. graminearum</span> (<span class="html-italic">Fg</span>), followed by chitosan_10 (CS) treatment two days later and a collection of samples 5 days later. Description of tested variants: <span class="html-italic">Hv</span>_mock—barley treated with mock solution; <span class="html-italic">Hv</span>_CS—barley treated with chitosan 200 ppm; <span class="html-italic">Hv_Fg</span>_CS—barley inoculated with <span class="html-italic">Fg</span> and treated with chitosan 200 ppm; and <span class="html-italic">Hv_Fg</span>—barley inoculated with <span class="html-italic">Fg</span>.</p>
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<p>Schematic timeline of biomass measurements and chitosan (CS) or salicylic acid (SA) treatments (<b>A</b>). Representative picture of barley plants grown in semi-hydroponics (<b>B</b>).</p>
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16 pages, 847 KiB  
Review
The Mechanisms of Developing Fungicide Resistance in Fusarium graminearum Causing Fusarium Head Blight and Fungicide Resistance Management
by Malini Anudya Jayawardana and Wannakuwattewaduge Gerard Dilantha Fernando
Pathogens 2024, 13(11), 1012; https://doi.org/10.3390/pathogens13111012 - 18 Nov 2024
Viewed by 748
Abstract
Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is one of the economically significant diseases in small grains. FHB causes severe damage to wheat production and grain quality. Several management strategies have been developed to control FHB, and chemical control through [...] Read more.
Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is one of the economically significant diseases in small grains. FHB causes severe damage to wheat production and grain quality. Several management strategies have been developed to control FHB, and chemical control through fungicides plays a significant role. Although fungicides have effectively controlled F. graminearum in the field, the continuous exposure causes a selection pressure in the pathogen population towards fungicide resistance. Several studies have identified fungicide-resistant F. graminearum isolates and fungicide-resistance mechanisms. Although new fungicides with a new mode of action can be introduced into the market, developing a new fungicide is time-consuming, and extra efforts are needed for testing, approvals, and registrations. Therefore, it is essential to strategize the methods to delay the fungicide resistance. This review focuses on the impact of several fungicide applications currently used on FHB, focusing on Fusarium graminearum, the status of the fungicide sensitivity for fungicide classes, the resistance mechanisms against fungicides, and the mitigation strategies to delay the development of fungicide resistance in the pathogen population. Studying the fungicide resistance mechanisms and the mitigation strategies will be helpful in the future to use the available fungicides against F. graminearum without losing its effectiveness. Full article
(This article belongs to the Special Issue Current Research on Fusarium: 2nd Edition)
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<p>The fungicide resistance mechanisms developed in the pathogen. The diagram shows how the sensitive and fungicide-resistant isolates react in the presence of fungicides.</p>
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15 pages, 4897 KiB  
Article
FgGET3, an ATPase of the GET Pathway, Is Important for the Development and Virulence of Fusarium graminearum
by Caihong Liu, Lu Lei, Jing Zhu, Lirun Chen, Shijing Peng, Mi Zhang, Ziyi Zhang, Jie Tang, Qing Chen, Li Kong, Youliang Zheng, Maria Ladera-Carmona, Karl-Heinz Kogel, Yuming Wei and Pengfei Qi
Int. J. Mol. Sci. 2024, 25(22), 12172; https://doi.org/10.3390/ijms252212172 - 13 Nov 2024
Viewed by 584
Abstract
GET3 is an ATPase protein that plays a pivotal role in the guided entry of the tail-anchored (GET) pathway. The protein facilitates the targeting and inserting of tail-anchored (TA) proteins into the endoplasmic reticulum (ER) by interacting with a receptor protein complex on [...] Read more.
GET3 is an ATPase protein that plays a pivotal role in the guided entry of the tail-anchored (GET) pathway. The protein facilitates the targeting and inserting of tail-anchored (TA) proteins into the endoplasmic reticulum (ER) by interacting with a receptor protein complex on the ER. The role of GET3 in various biological processes has been established in yeast, plants, and mammals but not in filamentous fungi. Fusarium graminearum is the major causal agent of Fusarium head blight (FHB), posing a threat to the yield and quality of wheat. In this study, we found that FgGET3 exhibits a high degree of sequence and structural conservation with its homologs across a wide range of organisms. Ectopic expression of FgGET3 in yeast restored the growth defects of the Saccharomyces cerevisiae ScGET3 knock-out mutant. Furthermore, FgGET3 was found to dimerize and localize to the cytoplasm, similar to its homologs in other species. Deletion of FgGET3 in F. graminearum results in decreased fungal growth, fragmented vacuoles, altered abiotic stress responses, reduced conidia production, delayed conidial germination, weakened virulence on wheat spikes and reduced DON production. Collectively, these findings underscore the critical role of FgGET3 in regulating diverse cellular and biological functions essential for the growth and virulence of F. graminearum. Full article
(This article belongs to the Special Issue Plant Pathogen Interactions: 2nd Edition)
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<p>Identification of the FgGET3 protein in <span class="html-italic">Fusarium graminearum</span>. The aa sequence of FgGET3 (XP_011318797.1), ScGET3 (NP_010183.1) of <span class="html-italic">Saccharomyces cerevisiae</span>, AtGET3a (NP_563640.1) of <span class="html-italic">Arabidopsis thaliana</span>, HsGET3 (NP_004308.1) of <span class="html-italic">Homo sapiens</span> and PfGET3 (XP_001351457.1) of <span class="html-italic">Plasmodium falciparum</span> was aligned using Clustal W2. ESPript 3.0 was used to highlight identical (white font, highlighted in red with red), well-conserved (red font, boxed in blue) residues. The conserved motifs are labeled above with blue lines and font. The sequence involved in both the GET1-GET3 and GET2-GET3 interactions is enclosed in a black frame. The other aa involved in the GET1-GET3 interaction are indicated with blue inverted triangles above. The aa involved in the GET4-GET3 interaction are indicated with black inverted triangles.</p>
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<p>Complementation of <span class="html-italic">S. cerevisiae</span> strain ∆<span class="html-italic">get3</span> with <span class="html-italic">FgGET3</span>. The plasmid pYES2-FgGET3 was transformed into ∆<span class="html-italic">get3</span>. The cell growth (ten-fold dilutions of a starting concentration of OD<sub>600</sub> = 1) without (CK) and in the presence of 3 mM CuSO<sub>4</sub> at 37 °C or 200 mM hygromycin at 30 °C on YPD plates is shown. The empty pYES2 vector was transformed into ∆<span class="html-italic">get3</span> as a negative control. The growth of each strain was examined after 3 days of incubation. Scale bar = 5 mm.</p>
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<p>Detection of FgGET3 homodimerization using the yeast two-hybrid assay. Y2HGold strains transformed with the pair of bait and prey vectors were grown on DDO and QDO/A media (ten-fold dilutions of a starting concentration of OD<sub>600</sub> = 1). The interactions of BD-53/AD-T and BD-Lam/AD-T were the positive and negative controls, respectively. AD: pGADT7; BD: pGBKT7; DDO: Double dropout medium (SD-Leu-Trp); QDO/A: Quadruple dropout medium (SD-Ade-His-Leu-Trp) supplemented with 70 ng/mL Aureobasidin A. Scale bar = 5 mm.</p>
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<p>Subcellular localization of FgGET3 in (<b>A</b>) ungerminated conidia and (<b>B</b>) conidia germinating for 8 h. Scale bar = 10 µm; DIC, differential interference contrast.</p>
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<p><span class="html-italic">FgGET3</span> in involved in vegetative growth and vacuole morphology. (<b>A</b>) Colony morphology of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C on PDA and mSNA plates. Photos were taken after incubation at 25 °C for 4 days. Scale bar = 2 cm. (<b>B</b>) Colony diameters of indicated strains on mSNA and PDA media. Means and standard deviations were calculated from three replicates; the asterisks indicate significant differences from the WT group (Student’s <span class="html-italic">t</span>-test, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) Microscopic observation of hyphal branching patterns of WT, and Δ<span class="html-italic">Fgget3</span> grown on PDA medium for 2 days. Scale bar = 20 µm. (<b>D</b>) Fluorescence microscopy images of WT and Δ<span class="html-italic">Fgget3</span> hyphae stained with the vacuole tracker dye CMAC. DIC: differential interference contrast. Scale bar = 20 µm. (<b>E</b>) Vacuole structures in hyphae of WT and Δ<span class="html-italic">Fgget3</span> observed using transmission electron microscopy. Scale bar = 1 µm.</p>
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<p><span class="html-italic">FgGET3</span> impacts the responses of <span class="html-italic">F. graminearum</span> to various stresses. (<b>A</b>) Colony morphology of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C in response to environmental stresses including 1M Sorbitol, 1 M NaCl, 0.5 mM Congo Red (CR), 0.025% SDS, 0.05% H<sub>2</sub>O<sub>2</sub> and high temperature at 33 °C. Scale bar = 2 cm. (<b>B</b>) Colony morphology of indicated strains in response to fungicides including 0.4 µg/mL carbendazim, 5 µg/mL tebuconazole, and 0.6 µg/mL pyraclostrobin. Scale bar = 2 cm. (<b>C</b>) Colony morphology of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C in response to 5 mM DTT. Scale bar = 2 cm. (<b>D</b>) Percentage of mycelium growth inhibition by environmental stresses. (<b>E</b>) Percentage of mycelium growth inhibition by fungicides. (<b>F</b>) Percentage of mycelium growth inhibition by DTT. Means and standard deviations were calculated from three replicates; the asterisks indicate significant differences from the WT group (Student’s <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p><span class="html-italic">FgGET3</span> is crucial for conidiation, conidial morphology, and germination of <span class="html-italic">F. graminearum</span>. (<b>A</b>) Morphology of phialide (white arrows) and conidia of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C strains. Scale bar = 20 μm. (<b>B</b>) Conidiation capacity of the indicated strains in CMC liquid medium. (<b>C</b>) Conidial length of the indicated strains. Means and standard deviations were calculated from three replicates, at least 100 conidia were observed in each replicate, and the asterisks indicate significant differences from the WT group. (Student’s <span class="html-italic">t</span>-test, *** <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>) Percentages of conidia with different numbers of septa in the indicated strains. At least 300 conidia were observed from three replicates. (<b>E</b>) Morphology of germinated conidia at 3 h, 6 h, and 9 h in liquid YEPD medium. Scale bar = 20 μm. (<b>F</b>) Germination rates of the indicated strains under a microscope after 6 h and 9 h incubation in YEPD liquid medium. At least 100 conidia were randomly observed at each time point.</p>
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<p><span class="html-italic">FgGET3</span> is crucial for conidiation, conidial morphology, and germination of <span class="html-italic">F. graminearum</span>. (<b>A</b>) Morphology of phialide (white arrows) and conidia of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C strains. Scale bar = 20 μm. (<b>B</b>) Conidiation capacity of the indicated strains in CMC liquid medium. (<b>C</b>) Conidial length of the indicated strains. Means and standard deviations were calculated from three replicates, at least 100 conidia were observed in each replicate, and the asterisks indicate significant differences from the WT group. (Student’s <span class="html-italic">t</span>-test, *** <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>) Percentages of conidia with different numbers of septa in the indicated strains. At least 300 conidia were observed from three replicates. (<b>E</b>) Morphology of germinated conidia at 3 h, 6 h, and 9 h in liquid YEPD medium. Scale bar = 20 μm. (<b>F</b>) Germination rates of the indicated strains under a microscope after 6 h and 9 h incubation in YEPD liquid medium. At least 100 conidia were randomly observed at each time point.</p>
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<p><span class="html-italic">FgGET3</span> contributes to the virulence and DON production of <span class="html-italic">F. graminearum</span>. (<b>A</b>) Fungal biomass in the inoculated spikelets was quantified using quantitative PCR (qPCR) at 2 dpi. The relative amount of fungal DNA to spikelet DNA was determined by comparing the <span class="html-italic">F. graminearum FgTUB2</span> gene to the wheat <span class="html-italic">TaGAPDH</span> gene using qPCR. The measurement was referred to as the value 1.0 obtained for the WT treatment. (<b>B</b>) The symptoms in spikelets, seeds, and rachises of infected wheat spikes infected with a conidial suspension of WT, Δ<span class="html-italic">Fgget3</span>, and Δ<span class="html-italic">Fgget3</span>-C were observed at 14 dpi. (<b>C</b>) The numbers of infected and bleached spikelets were counted at 14 dpi. (<b>D</b>) DON production in wheat spikes infected with conidia suspensions of indicated strains was quantified at 8 dpi. (<b>E</b>) The DON concentration of indicated strains was determined in a liquid medium. Means and standard deviations were calculated from three replicates; the asterisks indicate significant differences from the WT group (Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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17 pages, 2588 KiB  
Article
Structure and Fungicidal Activity of Secondary Metabolites Isolated from Trichoderma hamatum b-3
by Li Huang, Qiang Bian, Mengdan Liu, Yiwen Hu, Lijuan Chen, Yucheng Gu, Qiwei Zu, Guangzhi Wang and Dale Guo
J. Fungi 2024, 10(11), 755; https://doi.org/10.3390/jof10110755 - 31 Oct 2024
Viewed by 611
Abstract
Two new harziane diterpenes (12), five undescribed cyclonerane sesquiterpenes (37), and three known compounds, 11-cycloneren-3, 7, 10-triol (8), harziandione (9), and dehydroacetic acid (10), were isolated from Trichoderma hamatum [...] Read more.
Two new harziane diterpenes (12), five undescribed cyclonerane sesquiterpenes (37), and three known compounds, 11-cycloneren-3, 7, 10-triol (8), harziandione (9), and dehydroacetic acid (10), were isolated from Trichoderma hamatum b-3. Their structures were elucidated via comprehensive inspection of spectral evidence in HRESIMS and 1D and 2D NMR, and the absolute configuration of 18 was confirmed by NMR, ECD calculation, as well as Mosher’s method. In vitro fungicidal activity showed that some compounds showed great inhibitory activity against pathogenic fungi, including Fusarium graminearum, Sclerotinia sclerotiorum, Botrytis cinerea, and Rhizoctonia solani, among which compound 10 showed 100% inhibition of S. sclerotiorum and B. cinerea. The in vivo activity test showed that compound 10 was 65.8% effective against B. cinerea and compound 10 can be used as a lead compound for the development of biopesticides that inhibit B. cinerea. This study elucidated the bioactivity of secondary metabolites of T. hamatum and indicated the direction for the subsequent development of the biological control activity of T. hamatum. Full article
(This article belongs to the Special Issue Trichoderma in Action)
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<p>Key HMBC (bold lines) and <sup>1</sup>H–<sup>1</sup>H COSY (arrows) correlations of compounds <b>1</b>–<b>8</b>.</p>
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<p>Chemical structures of compounds <b>1</b>–<b>10</b>.</p>
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<p>Calculated ECD spectra and experimental ECD curves of compounds <b>1</b>–<b>4</b> in MeOH.</p>
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<p>Key NOESY correlations of compounds <b>1</b>–<b>8</b>.</p>
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<p>Calculated ECD spectra and experimental ECD curves of compounds <b>5</b>–<b>8</b> in MeOH.</p>
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<p>Δ<span class="html-italic">δ<sub>S-R</sub></span> value (ppm) of the MTPA ester of <b>8</b>.</p>
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<p>Activities of compound <b>10</b> against <span class="html-italic">B. cinerea</span> and <span class="html-italic">S. sclerotiorum</span> in vivo (CK: blank control; PC: positive control).</p>
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20 pages, 6293 KiB  
Article
FgUbiH Is Essential for Vegetative Development, Energy Metabolism, and Antioxidant Activity in Fusarium graminearum
by Jinwen Ge, Huanchen Zhai, Lei Tang, Shuaibing Zhang, Yangyong Lv, Pingan Ma, Shan Wei, Yu Zhou, Xiaofu Wu, Yang Lei, Fengguang Zhao and Yuansen Hu
Microorganisms 2024, 12(10), 2093; https://doi.org/10.3390/microorganisms12102093 - 20 Oct 2024
Viewed by 887
Abstract
Fusarium head blight in wheat is mainly caused by Fusarium graminearum and results in significant economic losses. Coenzyme Q (CoQ) is ubiquitously produced across organisms and functions as a hydrogen carrier in energy metabolism. While UbiH in Escherichia coli serves as a hydroxylase [...] Read more.
Fusarium head blight in wheat is mainly caused by Fusarium graminearum and results in significant economic losses. Coenzyme Q (CoQ) is ubiquitously produced across organisms and functions as a hydrogen carrier in energy metabolism. While UbiH in Escherichia coli serves as a hydroxylase in CoQ biosynthesis, its role in phytopathogenic fungi is not well understood. This study explored the role of the hydroxylase FgUbiH in F. graminearum. Using a FgUbiH deletion mutant, we observed reduced hyphal growth, conidial production, germination, toxin synthesis, and pathogenicity compared to the wild-type. A transcriptome analysis indicated FgUbiH’s involvement in regulating carbohydrate and amino acid metabolism. Deletion of FgUbiH impaired mitochondrial function, reducing adenosine triphosphate synthesis and increasing reactive oxygen species. Additionally, genes related to terpene skeleton synthesis and aldehyde dehydrogenase were downregulated. Our results underscore the importance of FgUbiH in F. graminearum’s growth, toxin production, and energy metabolism, aiding in the development of strategies for disease management. Full article
(This article belongs to the Special Issue Plant Pathogens: Monitoring, Identification and Biological Control)
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Figure 1
<p>Phylogenetic analysis of FgUbiH. Phylogenetic analysis of full-length amino acid sequences of FgUbiH and its orthologs from <span class="html-italic">Fusarium poae</span>, <span class="html-italic">Fusarium sporotrichioides</span>, <span class="html-italic">Fusarium graminearum</span>, <span class="html-italic">Fusarium equiseti</span>, <span class="html-italic">Fusarium tricinctum</span>, <span class="html-italic">Fusarium oxysporum</span>, <span class="html-italic">Fusarium verticillioides</span>, <span class="html-italic">Escherichia coli</span>, <span class="html-italic">Saccharomyces cerevisiae</span>, <span class="html-italic">Penicillium chrysogenum</span>, <span class="html-italic">Cryptococcus neoformans</span>, <span class="html-italic">Neurospora crassa</span>, <span class="html-italic">Colletotrichum sublineola</span>, <span class="html-italic">Botrytis cinerea</span>, <span class="html-italic">Sclerotinia sclerotiorum</span>, <span class="html-italic">Schizosaccharomyces pombe</span>, <span class="html-italic">EBlumeria graminis</span>, <span class="html-italic">Nicotiana tabacum</span>, <span class="html-italic">Aspergillus nidulans</span>, <span class="html-italic">Colletotrichum orbiculare</span>, <span class="html-italic">Pyricularia oryzae</span>, <span class="html-italic">Ustilago maydis</span>, <span class="html-italic">Drosophila melanogaster</span>, <span class="html-italic">Coprinopsis cinerea</span>, <span class="html-italic">Microdochium nivale</span>, <span class="html-italic">Aspergillus flavus</span>, <span class="html-italic">Verticillium dahliae</span>. The phylogenetic tree was constructed using the neighbor-joining method with MEGA6 software. The bootstrap values displayed were calculated from 1000 replications. The presence of FgUbiH in <span class="html-italic">F. graminearum</span> is indicated by the black bold font.</p>
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<p>Construction of <span class="html-italic">FgUbiH</span> mutants and the impact of FgUbiH on vegetative growth. (<b>A</b>) <span class="html-italic">FgUbiH</span> deletion mutant construction. (<b>B</b>) The plasmid pKNT-FgUbiH-C was used to construct the <span class="html-italic">FgUbiH</span> complementation strain. (<b>C</b>) Wild-type PH-1, <span class="html-italic">FgUbiH</span> deletion mutant (∆<span class="html-italic">FgUbiH</span>), and <span class="html-italic">FgUbiH</span> complemented strains (∆<span class="html-italic">FgUbiH-C</span>) were grown on complete medium (CM), starch yeast medium (SYM), potato dextrose agar medium (PDA), and minimal media (MM) at 28 °C for 72 h. (<b>D</b>) The colony diameter of each strain. Significance was marked using “**” (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>FgUbiH influences conidial production and asexual development. (<b>A</b>) Conidia production of wild-type PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> strains in CMC liquid medium for 72 h. (<b>B</b>) Conidial morphology of the wild-type PH-1, ∆<span class="html-italic">FgUbiH</span> and ∆<span class="html-italic">FgUbiH-C</span> strains cultured in CMC liquid medium for 72 h. (<b>C</b>) Conidial germination of wild-type PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> strains at different periods. (<b>D</b>) Conidia of each strain were inoculated into the CM liquid medium, and germination rate of conidia was examined under a microscope every 2 h. (<b>E</b>) Images of the perithecia produced by the PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> strains after 21 days of inoculation on carrot agar plates. Significance was marked using “*” (<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>FgUbiH is essential for virulence display. (<b>A</b>) The pathogenicity of ∆<span class="html-italic">FgUbiH</span> mutant on flowering wheat heads showed a significant decrease compared with the wild-type PH-1 and ∆<span class="html-italic">FgUbiH-C</span> strains. Conidia suspensions from different strains were inoculated into flowering wheat heads in humid conditions, and disease symptoms were assessed after 7 days. The red arrow indicates the location for inoculating the spore suspension of the strain. (<b>B</b>) A decrease in deoxynivalenol (DON) content was observed in the ∆<span class="html-italic">FgUbiH</span> mutant strain. PH-1, ∆<span class="html-italic">FgUbiH,</span> and ∆<span class="html-italic">FgUbiH</span>-C strains were incubated in wheat grain for 21 days. (<b>C</b>) A reduction in DON content was noted in the ∆<span class="html-italic">FgUbiH</span> mutant strain. All of the strains were incubated in a trichothecene biosynthesis induction (TBI) liquid medium for 28 days. Drying and weighing were conducted on the mycelia of each strain to determine the fungal biomass. (<b>D</b>) In the ∆<span class="html-italic">FgUbiH</span> mutant, the relative expression levels of several TRI genes involved in trichothecene biosynthesis were notably reduced, except for TRI6. Significance was marked using “**” (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Transcriptomic analysis of ∆<span class="html-italic">FgUbiH</span> strain compared with wild-type PH-1 strain. (<b>A</b>) The up and down-regulated differentially expressed genes (DEGs) in the ∆<span class="html-italic">FgUbiH</span> mutant strain compared with the wild-type PH-1 strain. (<b>B</b>) Volcano plots of DEGs. W: wild-type PH-1 strain; U: ∆<span class="html-italic">FgUbiH</span> mutant strain. (<b>C</b>) Gene Ontology enrichment analysis of DEGs (Padj &lt; 0.05). (<b>D</b>) Kyoto Encyclopedia of Genes and Genomes analysis of DEGs. (<b>E</b>) Relative expression levels of genes related with tryptophan metabolism in wild-type PH-1 and <span class="html-italic">FgUbiH</span> mutant. RNA was extracted from the mycelia of each strain following a 3-day incubation in a CM. Significance was marked using “**” (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The impact of FgUbiH on pigment formation. (<b>A</b>) The pigment changes of wild-type, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> strains were observed after 72 h of inoculation on PDA plates and in a PDA liquid medium, respectively. (<b>B</b>) Relative expression levels of genes related to pigment synthesis in wild-type PH-1 and <span class="html-italic">FgUbiH</span> mutants were measured. RNA was extracted from the mycelia of each strain after a 3-day incubation in a PDA medium. Significance was marked using “**” (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Effects of <span class="html-italic">FgUbiH</span> deletion on antioxidant function in <span class="html-italic">F. graminearum</span>. (<b>A</b>) Wild-type PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> mutants grown on CM plates with or without 0.05% H<sub>2</sub>O<sub>2</sub>, 2.5% C<sub>2</sub>H<sub>5</sub>OH, and 0.1% CH<sub>3</sub>CHO for 3 days. (<b>B</b>) Growth inhibition rate of oxidative stress factors to each strain. (<b>C</b>) The enzyme activity of peroxidase (POD). (<b>D</b>) The enzyme activity of superoxide dismutase (SOD). (<b>E</b>) The enzyme activity of catalase (CAT). (<b>F</b>) The concentration of H<sub>2</sub>O<sub>2</sub>. (<b>G</b>) Reactive oxygen species (ROS) accumulation was observed using laser confocal scanning microscopy (LSCM) (FV3000, OLYMPUS Corporation, Tokyo, Japan). The wild-type PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> mutant strains were grown on CM medium at 28 °C for 72 h. The significance level was tested by unpaired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Effects of <span class="html-italic">FgUbiH</span> deletion on mitochondrial function. (<b>A</b>) Enzyme activity of mitochondrial dehydrogenase. (<b>B</b>) Enzyme activity of succinate dehydrogenase (SDH). (<b>C</b>) ATP content. (<b>D</b>) Fluorescence images of JC-1 staining of PH-1, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> mycelia were observed using LSCM. A high ratio of Red fluorescence was observed, indicating the formation of J-aggregates in the mitochondrial matrix and a high mitochondrial membrane potential (MMP). The presence of a high level of green fluorescence indicated that JC-1 was in a monomeric state, signifying a low MMP. The yellow fluorescence indicated that some JC-1 probes are in an intermediate state, suggesting that the changes in MMP were not significant. The wild-type PH-1 strain, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> mutant strains were cultivated in CM for 72 h, following which mycelia samples were harvested for analysis. The significance level was tested by unpaired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The impact of FgUbiH on coenzyme Q synthesis and aldehyde dehydrogenase (ALDH) activity. (<b>A</b>) The relative expression levels of genes involved in terpene skeleton synthesis in wild-type strains and <span class="html-italic">FgUbiH</span> mutant strains. (<b>B</b>) The activity of acetaldehyde dehydrogenase in wild-type and <span class="html-italic">FgUbiH</span> mutant strains. (<b>C</b>) The relative expression levels of ALDH genes in wild-type and <span class="html-italic">FgUbiH</span> mutant strains. The wild-type PH-1 strain, ∆<span class="html-italic">FgUbiH</span>, and ∆<span class="html-italic">FgUbiH-C</span> mutant strains were cultivated in CM liquid medium at 28 °C and 150 rpm for 72 h, following which mycelia samples were harvested for analysis. The significance level was tested by unpaired <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Proposed model for the role FgUbiH in <span class="html-italic">Fusarium graminearum</span>. The red arrow indicates downregulation in ΔFgUbiH strain compared to control, while the blue arrow signifies upregulation in certain parameters.</p>
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20 pages, 10453 KiB  
Article
Fusarium Species Shifts in Maize Grain as a Response to Climatic Changes in Poland
by Elzbieta Czembor, Seweryn Frasiński, Monika Urbaniak, Agnieszka Waśkiewicz, Jerzy H. Czembor and Łukasz Stępień
Agriculture 2024, 14(10), 1793; https://doi.org/10.3390/agriculture14101793 - 12 Oct 2024
Viewed by 1188
Abstract
Maize, along with wheat and rice, is the most important crop for food security. Ear rots caused by Fusarium species are among the most important diseases of maize. The distribution of Fusarium species provides essential epidemiological information for disease management. The effect of [...] Read more.
Maize, along with wheat and rice, is the most important crop for food security. Ear rots caused by Fusarium species are among the most important diseases of maize. The distribution of Fusarium species provides essential epidemiological information for disease management. The effect of weather conditions, climate change and geographic localization on the Fusarium population in Poland was evaluated between 2015 and 2018. Grain samples (n = 233) were collected from hybrids at 16 locations (L1–L16). The differences in altitude between locations ranged from 39 to 243 m above sea level, longitude varied between 15°55′ and 23°12′ E, and latitude spanned from 50°12′ to 54°01′ N. Isolates were identified using molecular techniques. The highest Fusarium species frequency was recorded for 2016 (30.70%) and 2017 (28.18%), and the lowest for 2018 (5.36%). F. verticillioides and F. temperatum were the most frequent. Altitude has an effect on F. vericillioides frequency: F. graminearum showed a negative correlation with both latitude and longitude. In Polish conditions, from silking to harvesting, the number of days with higher precipitation and lower temperatures is associated with an increased frequency of F. verticillioides, F. temperatum, F. graminearum and F. avenaceum. This suggests that the Fusarium presence in Poland is significantly influenced not only by climate change but also by extreme weather changes. Full article
(This article belongs to the Special Issue Identification and Management of Fungal Plant Pathogens)
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Figure 1
<p>Geographic localities where pathogen surveys were conducted (grain samples collected). Localities were visualized using the GinkoMaps project (<a href="http://www.ginkgomaps.com" target="_blank">http://www.ginkgomaps.com</a> accessed on 9 October 2024). Grain samples collected in localities (L1–L16) represented the north-western (NW), north-eastern (NE), central (C), central-western (CW), south-eastern (SE) and south-western (SW) regions of Poland. The SW region was represented by L1 sampled during 2015–2018, L2 sampled from 2016 to 2018 and L3 in 2016 and 2017; the NW region was represented by L5 sampled during 2015–2018, and L15 and L16 sampled in 2015; the SE was represented by L4 sampled in 2018; the NE was represented by L13 and L14 sampled 2015–2018; and the C region was represented by L6, L9 sampled during 2015–2018 and L11 sampled in 2018.</p>
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<p>Average frequency of <span class="html-italic">Fusarium</span> isolates isolated from <span class="html-italic">n</span> = 233 grain samples, each sample representing <span class="html-italic">n</span> = 50 grains, collected from sixteen localities in Poland individually during 2015 (<span class="html-italic">n</span> = 43), 2016 (<span class="html-italic">n</span> = 56), 2017 (<span class="html-italic">n</span> = 86) and 2018 (<span class="html-italic">n</span> = 50).</p>
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<p>Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates isolated from <span class="html-italic">n</span> = 233 grain samples, each sample representing <span class="html-italic">n</span> = 50 grains, collected from sixteen localities during 2015 (<span class="html-italic">n</span> = 54), 2016 (<span class="html-italic">n</span> = 54), 2017 (<span class="html-italic">n</span> = 86) and 2018 (<span class="html-italic">n</span> = 50). Bars represent standard deviation (<span class="html-italic">SD</span>).</p>
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<p>Average and maximum frequency of <span class="html-italic">Fusarium</span> species individually isolated from <span class="html-italic">n</span> = 233 grain samples, each sample representing <span class="html-italic">n</span> = 50 grains, collected from sixteen localities in 2015, 2016, 2017 and 2018, respectively. (<b>A</b>) Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates in 2015 (<span class="html-italic">n</span> = 43). (<b>B</b>) Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates in 2016 (<span class="html-italic">n</span> = 54). (<b>C</b>) Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates in 2017 (<span class="html-italic">n</span> = 86). (<b>D</b>) Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates in 2018 (<span class="html-italic">n</span> = 50). Bars represent standard deviation (<span class="html-italic">SD</span>).</p>
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<p>Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates isolated from <span class="html-italic">n</span> = 233 grain samples, each sample representing <span class="html-italic">n</span> = 50 grains, collected from sixteen localities (L) during 2015 (<span class="html-italic">n</span> = 43), 2016 (<span class="html-italic">n</span> = 54), 2017 (<span class="html-italic">n</span> = 86) and 2018 (<span class="html-italic">n</span> = 50). Bars represent standard deviation (<span class="html-italic">SD</span>).</p>
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<p>Average and maximum frequency of <span class="html-italic">Fusarium</span> isolates isolated from <span class="html-italic">n</span> = 233 grain samples, each sample representing <span class="html-italic">n</span> = 50 grains, collected from sixteen localities (L) individually in 2015, 2016, 2017 and 2018, respectively. (<b>A</b>) Average and maximum <span class="html-italic">Fusarium</span> isolate frequency in 2015 (<span class="html-italic">n</span> = 43). (<b>B</b>) Average and maximum <span class="html-italic">Fusarium</span> isolate frequency in 2016 (<span class="html-italic">n</span> = 54). (<b>C</b>) Average and maximum <span class="html-italic">Fusarium</span> isolate frequency in 2017 (<span class="html-italic">n</span> = 86). (<b>D</b>) Average and maximum <span class="html-italic">Fusarium</span> isolate frequency in 2018 (<span class="html-italic">n</span> = 50). Bars represent standard deviation (<span class="html-italic">SD</span>).</p>
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<p>Projections of the scores for the frequency of <span class="html-italic">Fusarium</span> isolated from grain samples in 2015 and weather conditions in sampled localities onto the PC1 and PC2 factor planes, and projections of the scores for localities (L) onto the PC1 and PC2 factor planes in 2015. (<b>A</b>) PCA score plot of PC1 and PC2 factors representing correlations among the frequency of <span class="html-italic">Fusarium</span> species isolates (%) isolated from <span class="html-italic">n</span> = 43 grain samples (<span class="html-italic">n</span> = 50 grains per sample) and weather conditions in the sampled localities (number of days with temperatures T &gt; 22 °C, T: 19–22 °C and T &lt; 19 °C, and number of days with precipitation above 0.0 mm/m<sup>2</sup> during maize reproductive stages from R1, silking time stage, to R6, physiological maturity stage—June, July, August, September). (<b>B</b>) PCA score plot of PC1 and PC2 factors representing the relationship between localities (L1–L11) based on isolated <span class="html-italic">Fusarium</span> spp. frequency and weather condition data.</p>
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<p>Projections of the scores for the frequency of <span class="html-italic">Fusarium</span> isolated from grain samples in 2016 and weather conditions in sampled localities onto the PC1 and PC2 factor planes, and projections of the scores for localities (L) onto the PC1 and PC2 factor planes in 2016. (<b>A</b>) PCA score plot of PC1 and PC2 factors representing correlations among the frequency of <span class="html-italic">Fusarium</span> species isolates (%) isolated from <span class="html-italic">n</span> = 54 grain samples (<span class="html-italic">n</span> = 50 grains per sample) and weather conditions in sampled localities (number of days with temperatures T &gt; 22 °C, T: 19–22 °C and T &lt; 19 °C, and number of days with precipitation above 0.0 mm/m<sup>2</sup> during maize reproductive stages from R1,silking time stage, to R6, physiological maturity stage—June, July, August, September). (<b>B</b>) PCA score plot of PC1 and PC2 factors representing the relationship between localities (L1–L14 except L5 and L11) based on isolated <span class="html-italic">Fusarium</span> spp. frequency and weather condition data.</p>
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<p>Projections of the scores for the frequency of <span class="html-italic">Fusarium</span> isolated from grain samples in 2017 and weather conditions in sampled localities onto the PC1 and PC2 factor planes, and projections of the scores for localities (L) onto the PC1 and PC2 factor planes in 2017. (<b>A</b>) PCA score plot of PC1 and PC2 factors representing correlations among the frequency of <span class="html-italic">Fusarium</span> species isolates (%) isolated from <span class="html-italic">n</span> = 86 grain samples (<span class="html-italic">n</span> = 50 grains per sample) and weather conditions in sampled localities (number of days with temperatures T &gt; 22 °C, T: 19–22 °C and T &lt; 19 °C, and number of days with precipitation above 0.0 mm/m<sup>2</sup> during maize reproductive stages from R1, silking time stage, to R6, physiological maturity stage—June, July, August, September). (<b>B</b>) PCA score plot of PC1 and PC2 factors representing the relationship between localities (L1–L12 except L10) based on isolated <span class="html-italic">Fusarium</span> spp. frequency and weather condition data.</p>
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<p>Projections of the scores for the frequency of <span class="html-italic">Fusarium</span> isolated from grain samples in 2018 and weather conditions in sampled localities onto the PC1 and PC2 factor planes, and projections of the scores for localities (L) onto the PC1 and PC2 factor planes in 2018. (<b>A</b>) PCA score plot of PC1 and PC2 factors representing correlations among the frequency of <span class="html-italic">Fusarium</span> species isolates (%) isolated from <span class="html-italic">n</span> = 50 grain samples (<span class="html-italic">n</span> = 50 grains per) and weather conditions in sampled localities (number of days with temperatures T &gt; 22 °C, T: 19–22 °C and T &lt; 19 °C, and number of days with precipitation above 0.0 mm/m<sup>2</sup> during maize reproductive stages from R1, silking time stage, to R6, physiological maturity stage—une, July, August, September). (<b>B</b>) PCA score plot of PC1 and PC2 factors representing the relationship between localities (L1–L12 except L8) based on isolated <span class="html-italic">Fusarium</span> spp. frequency and weather condition data.</p>
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<p>Projections of the scores for <span class="html-italic">Fusarium</span> frequency isolates isolated from grain samples (<span class="html-italic">n</span> = 233, <span class="html-italic">n</span> = 50 grains in each sample) using PCA and geographic localization data variables (latitude [(ϕ)], longitude [λ], altitude [m.pm]). (<b>A</b>) PCA score plot of PC1 and PC2 factors representing correlations among the frequency of <span class="html-italic">Fusarium</span> species isolates and geographic localization data variables. (<b>B</b>) PCA score plot of PC1 and PC2 factors representing the relationship between localities (L1–L15) based on isolated <span class="html-italic">Fusarium</span> spp. frequency and geographic localization data variables.</p>
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15 pages, 3900 KiB  
Article
An Antisense Long Non-Coding RNA, LncRsn, Is Involved in Sexual Reproduction and Full Virulence in Fusarium graminearum
by Zhizhen Fu, Yanjie Chen, Gaolei Cai, Huijuan Peng, Xiaoyu Wang, Ping Li, Aiguo Gu, Yanli Li and Dongfang Ma
J. Fungi 2024, 10(10), 692; https://doi.org/10.3390/jof10100692 - 3 Oct 2024
Viewed by 1162
Abstract
Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is a devastating crop disease that leads to significant declines in wheat yield and quality worldwide. Long non-coding RNAs (lncRNAs) are found to play significant functions in various biological processes, but their regulatory [...] Read more.
Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is a devastating crop disease that leads to significant declines in wheat yield and quality worldwide. Long non-coding RNAs (lncRNAs) are found to play significant functions in various biological processes, but their regulatory functions in the sexual reproduction and pathogenicity of F. graminearum have not been studied extensively. This study identified an antisense lncRNA, named lncRsn, located in the transcription initiation site region between the 5′-flanking gene FgSna and the 3′-flanking gene FgPta. A deletion mutant of lncRsn (ΔlncRsn) was constructed through homologous recombination. ΔlncRsn exhibited huge reductions in pathogen and sexual reproduction. Additionally, the deletion of lncRsn disrupted the biosynthesis of deoxynivalenol (DON) and impaired the formation of infection structures. RT-qPCR analysis reveals that lncRsn may negatively regulate the transcription of the target gene FgSna. This study found that lncRsn plays an important role in sexual and asexual reproduction, pathogenicity, virulence, osmotic stress, and cell wall integrity (CWI) in F. graminearum. Further characterization of pathogenesis-related genes and the reaction between lncRsn and protein-coding genes will aid in developing novel approaches for controlling F. graminearum diseases. Full article
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Figure 1

Figure 1
<p>Identification and analysis of lncRsn, and its flanking genes <span class="html-italic">FgSna</span>, and <span class="html-italic">FgPta</span>. (<b>a</b>) Analysis of the FgPta protein sequence (1559 aa) reveals the presence of a PhoLip_ATPase_N domain (202–276 aa, grey), an E1-E2_ATPase domain (353–593 aa, grey), a Cation_ATPase domain (750–867 aa, grey), a PhoLip_ATPase_N domain, seven transmembrane helices (green); (<b>b</b>) Analysis of the <span class="html-italic">FgSna</span> protein sequence (236 aa) shows the presence of a Longin domain and has one transmembrane helix (green); (<b>c</b>) Strategy and identification of replacement of lncRsn with hygromycin gene (<span class="html-italic">Hygr</span> gene) in the wild-type strain PH-1 of <span class="html-italic">F. graminearum</span>. (<b>d</b>) The genomic location of lncRsn and the transcript isoforms of <span class="html-italic">FgSna</span> and <span class="html-italic">FgPta</span>. There are six transcript isoforms of <span class="html-italic">FgPta</span> based on their transcript lengths. <span class="html-italic">FgSna</span> has one transcript isoform. TIS: translation initiation site; TTS: transcription termination site.</p>
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<p>LncRsn is essential for asexual reproduction in <span class="html-italic">F. graminearum</span>. (<b>a</b>) Morphology of the sexual fruiting bodies (perithecia) produced by the wild-type PH-1 strain and the lncRsn deletion mutant (ΔlncRsn) strain. Photographs were taken using a 1000 μm scale bar; (<b>b</b>) Perithecia, ascus formation, and ascospore discharge photographed at 14 dpi; (<b>c</b>) Quantification of the number of perithecia and the ratio of perithecia with appendages (whips) in the wild-type and mutant strains, (<span class="html-italic">p</span> &lt; 0.01); (<b>d</b>) Ascospores from the different strains, significant differences between the replicates are represented by letter a and b.</p>
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<p>Roles of the lncRsn in fungal pathogenicity. (<b>a</b>) Wheat spikelets inoculated with each strain and observed at 14 days after inoculation; (<b>b</b>) Wheat coleoptiles inoculated with each strain and observed at 7 days after inoculation; (<b>c</b>) Corn silks were inoculated with each strain and observed at 5 days after inoculation, <span class="html-italic">p</span> &lt; 0.01, significant differences between the replicates are represented by ****.</p>
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<p>Roles of the lncRsn in mycelium penetration, infection structure, and DON production. (<b>a</b>) Mycelial penetration of wild-type PH-1 and ΔlncRsn strains on PSA medium after three days, with removal of aerial mycelium in cellophane after six days; (<b>b</b>) Infection structure formation of wild-type PH-1 and ΔlncRNA strains on wheat leaves after 24-48 h; (<b>c</b>) Analysis of DON toxin content in wheat kernels infected by different strains. (<b>d</b>) Analysis of DON toxin content of the strains in the culture supernatant incubated after seven days of TBI, <span class="html-italic">p</span> &lt; 0.05, significant differences between the replicates are represented by letter a and b.</p>
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<p>lncRsn is not necessary for asexual reproduction but displays sensitivity under stress conditions. (<b>a</b>) Colony morphology and diameter of PH-1 and ΔlncRsn on PSA medium; (<b>b</b>) Fungal growth assessment on CM plates with 0.05% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) H<sub>2</sub>O<sub>2</sub>, Congo Red, and SDS. Stress growth inhibition rate analysis;(<b>c</b>) Conidial germination of lncRsn at a 25 µm scale bar, significant differences between the replicates are represented by letter a and b.</p>
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<p>The expression pattern of FgPta, lncRsn, and FgSna during the nutrient mycelium period and the pathogenic period. (<b>a</b>) The expression level of lncRsn in the mutant; (<b>b</b>) The expression level of lncRsn and its neighboring genes in the wild-type PH-1 during the pathogenic period; (<b>c</b>) The expression level of lncRsn and its neighboring genes in the mutant strain ΔlncRsn during the pathogenic period; (<b>d</b>) The expression level of FgSna and its neighboring genes in the PH-1 and mutant strain ΔlncRsn during the nutrient mycelium period and the pathogenic period; (<b>e</b>) The expression level of lncRsn and its neighboring genes in the PH-1 and mutant strain ΔlncRsn during the nutrient mycelium period and the pathogenic period. (<b>f</b>) The expression level of FgPta and its neighboring genes in the PH-1 and mutant strain ΔlncRsn during the nutrient mycelium period and the pathogenic period, significant differences between the replicates are represented by **, ***, ****, no significant differences between the replicates are represented by ns.</p>
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15 pages, 1174 KiB  
Article
Influence of Wheat Cultivars, Infection Level, and Climate after Anthesis on Efficacy of Fungicide for Control of Fusarium Head Blight in the Huang-Huai-Hai Plain of China
by Fei Xu, Hongqi Wang, Chaohong Feng, Ruijie Shi, Jihong Liu, Junmei Wang, Hua Fan, Lei Bai, Xiaoqing Li, Xiaoli Hu, Lijuan Li, Lulu Liu, Yahong Li, Zihang Han, Wei Liu, Yuli Song and Yilin Zhou
Agronomy 2024, 14(10), 2266; https://doi.org/10.3390/agronomy14102266 - 1 Oct 2024
Viewed by 596
Abstract
Fusarium head blight (FHB), caused by the Fusarium graminearum species complex, causes significant losses in grain yield and quality of wheat (Triticum aestivum) by inducing floret sterility. Grains become contaminated with mycotoxins, especially deoxynivalenol (DON), making them unsuitable for consumption. To [...] Read more.
Fusarium head blight (FHB), caused by the Fusarium graminearum species complex, causes significant losses in grain yield and quality of wheat (Triticum aestivum) by inducing floret sterility. Grains become contaminated with mycotoxins, especially deoxynivalenol (DON), making them unsuitable for consumption. To clarify the impact of wheat cultivar resistance, infection level, and climate after anthesis on the efficacy of a fungicide for the control of FHB, we treated two moderately susceptible cultivars and 11 susceptible cultivars with fungicide (48% phenamacril + tebuconazole) at anthesis over two years. FHB incidence (INC), disease severity index (DSI), Fusarium-damaged kernels, DON contamination, thousand-kernel weight, and yield were evaluated under artificially inoculated and naturally infected field trials in 2018 and 2021. The results of multi-factor variance analysis show that the control efficacy with respect to INC and DSI is affected by cultivar, fungicide, infection level, and climatic conditions including the average daily temperature, average daily relative humidity, and total rainfall from anthesis to 21 days after anthesis (p < 0.01). Notably, cultivar resistance (deviance = 13.34, 9.55, and 11.22) is more important than fungicide (deviance = 5.77, 6.66, and 6.69) to control the efficacy of INC, DSI, and DON. The results also suggest that infection level appears to be more important than cultivars and fungicide to control the efficacy of INC, and more important than fungicide to control the efficacy of DSI. Total rainfall is more important than other climatic factors. Our results reveal that fungicide is more effective in moderately susceptible cultivars (‘Zhengmai 9023’ and ‘Xinong 979’, 89.5%~98.9%) and some susceptible cultivars than in other susceptible cultivars (‘Zhengmai 7698’ and ‘Zhoumai 27’, 51.9%~67.2%). Thus, integrating cultivar resistance with fungicide application can be an effective strategy for the management of FHB and DON in winter wheat in the Huang-huai-hai Plain of China. Full article
(This article belongs to the Special Issue Mechanism and Sustainable Control of Crop Diseases)
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<p>Visible signs of Fusarium head blight infection diminished upon fungicide treatment under artificial field inoculation in the 2018 field experiments. Representative photographs of wheat spikes from the susceptible cultivar ‘Zhengmai 366’ (<b>A</b>,<b>B</b>), the susceptible cultivar ‘Zhoumai 18’ (<b>C</b>,<b>D</b>), and the moderately susceptible cultivar ‘Xinong 979’ (<b>E</b>,<b>F</b>) in artificially inoculated field trials without (<b>A</b>,<b>C</b>,<b>E</b>) or with the application of the Jingxing fungicide (36% [<span class="html-italic">w</span>/<span class="html-italic">v</span>] phenamacril + 12% [<span class="html-italic">w</span>/<span class="html-italic">v</span>] tebuconazole, 750 mL/ha) (<b>B</b>,<b>D</b>,<b>F</b>).</p>
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<p>Visible signs of Fusarium head blight infection diminished upon fungicide treatment under natural field inoculation in the 2018 field experiments. Representative photographs of wheat spikes from the susceptible cultivar ‘Zhengmai 366’ (<b>A</b>,<b>B</b>), the susceptible cultivar ‘Zhoumai 18’ (<b>C</b>,<b>D</b>), and the moderately susceptible cultivar ‘Xinong 979’ (<b>E</b>,<b>F</b>) under natural conditions without (<b>A</b>,<b>C</b>,<b>E</b>) or with the application of the Jingxing fungicide (36% [<span class="html-italic">w</span>/<span class="html-italic">v</span>] phenamacril + 12% [<span class="html-italic">w</span>/<span class="html-italic">v</span>] tebuconazole, 750 mL/ha) (<b>B</b>,<b>D</b>,<b>F</b>).</p>
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23 pages, 7005 KiB  
Article
Exploration of Volatileomics and Optical Properties of Fusarium graminearum-Contaminated Maize: An Application Basis for Low-Cost and Non-Destructive Detection
by Maozhen Qu, Changqing An, Fang Cheng and Jun Zhang
Foods 2024, 13(19), 3087; https://doi.org/10.3390/foods13193087 - 27 Sep 2024
Viewed by 732
Abstract
Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the [...] Read more.
Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200–1100 nm) were used to explore the optical properties of F. graminearum-contaminated maize. Volatile organic compounds (VOCs) of F. graminearum-contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400–600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of F. graminearum-contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection. Full article
(This article belongs to the Section Food Quality and Safety)
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<p>RSS-1 (<b>a</b>) and RSS-2 (<b>b</b>) containing a UV laser. RSS-1 (<b>c</b>) and RSS-2 (<b>d</b>) with VIS/NIR light source. TSS containing a UV laser (<b>e</b>) and a VIS/NIR light source (<b>f</b>).</p>
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<p>Composition of (<b>a</b>) reflection imaging platform, (<b>b</b>) transmission imaging platform, and (<b>c</b>). asymmetric polarization imaging platform.</p>
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<p>(<b>a</b>) Images of maize samples with different levels of <span class="html-italic">F. graminearum</span> contamination. (<b>b</b>) Change curves of fungal spores in maize with the growth of <span class="html-italic">F. graminearum</span>. <span class="html-italic">F. graminearum</span> spores observed in PDA medium (<b>c1</b>) and maize (<b>c2</b>) after magnified 400 times. Change curves of DON concentration (<b>d</b>) and ZEN concentration (<b>e</b>) in maize with the growth of <span class="html-italic">F. graminearum</span>.</p>
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<p>Detection of VOCs released from sample 1 (<b>a</b>), sample 2 (<b>b</b>), sample 3 (<b>c</b>), sample 4 (<b>d</b>), and sample 5 (<b>e</b>) by HS-SPME-GC-MS.</p>
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<p>(<b>a</b>) Image of sample 1 (untreated), sample 1 (sterilized with NaClO), and sample 1 (cultured for five days). (<b>b</b>) Average color intensity of the three samples.</p>
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<p>Average reflection spectra of RSS based on UV laser and integrating sphere (<b>a</b>), RSS based on VIS/NIR light source and integrating sphere (<b>b</b>), RSS based on UV laser and bifurcated optical fiber (<b>c</b>), and RSS based on VIS/NIR light source and bifurcated optical fiber (<b>d</b>). Average transmission spectra of TSS based on UV laser (<b>e</b>) and VIS/NIR light source (<b>f</b>).</p>
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<p>(<b>a</b>) Average UV excitation reflection spectra of five samples. (<b>b</b>) Magnified average UV excitation reflection spectra of five samples at the wavelength of 400–650 nm. (<b>c</b>) Average VIS/NIR reflection spectra of five samples. (<b>d</b>) Average VIS/NIR transmission spectra of five samples.</p>
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<p>(<b>a</b>) Sample images collected under three imaging platforms. (<b>b</b>) PCA score plot of UV excitation reflection imaging. (<b>c</b>) PCA score plot of VIS reflection imaging. (<b>d</b>) PCA score plot of transmission imaging.</p>
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<p>Confusion matrix based on UV excitation reflection imaging and SVM in the training set (<b>a</b>) and in the prediction set (<b>b</b>). Confusion matrix based on VIS reflection imaging and SVM in the training set (<b>c</b>) and in the prediction set (<b>d</b>). Confusion matrix based on transmission imaging and SVM in the training set (<b>e</b>) and in the prediction set (<b>f</b>).</p>
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<p>Confusion matrix based on UV excitation reflection imaging and SVM in the training set (<b>a</b>) and in the prediction set (<b>b</b>). Confusion matrix based on VIS reflection imaging and SVM in the training set (<b>c</b>) and in the prediction set (<b>d</b>). Confusion matrix based on transmission imaging and SVM in the training set (<b>e</b>) and in the prediction set (<b>f</b>).</p>
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<p>(<b>a</b>) Sample images collected using asymmetric polarization platform. (<b>b</b>) PCA score plot of VIS reflection imaging after reducing maize categories. (<b>c</b>) PCA score plot of transmission imaging after reducing maize categories. (<b>d</b>) PCA score plot of asymmetric polarization imaging after reducing maize categories.</p>
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<p>Confusion matrix (only three levels of maize) based on VIS reflection imaging and SVM in the training set (<b>a</b>) and in the prediction set (<b>b</b>). Confusion matrix (only three levels of maize) based on transmission imaging and SVM in the training set (<b>c</b>) and in the prediction set (<b>d</b>). Confusion matrix (only three levels of maize) based on asymmetric polarization imaging and SVM in the training set (<b>e</b>) and in the prediction set (<b>f</b>).</p>
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<p>Confusion matrix (only three levels of maize) based on VIS reflection imaging and SVM in the training set (<b>a</b>) and in the prediction set (<b>b</b>). Confusion matrix (only three levels of maize) based on transmission imaging and SVM in the training set (<b>c</b>) and in the prediction set (<b>d</b>). Confusion matrix (only three levels of maize) based on asymmetric polarization imaging and SVM in the training set (<b>e</b>) and in the prediction set (<b>f</b>).</p>
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13 pages, 1656 KiB  
Article
Pathogenicity and Metabolomic Characterization of Fusarium graminearum and Fusarium poae Challenge in Barley under Controlled Conditions
by Raja Khanal, Kerin Hudson, Adam Foster, Xiben Wang, Elizabeth K. Brauer, Thomas E. Witte and David P. Overy
J. Fungi 2024, 10(10), 670; https://doi.org/10.3390/jof10100670 - 26 Sep 2024
Cited by 1 | Viewed by 758
Abstract
Barley is the third most important cereal crop in terms of production in Canada, and Fusarium head blight (FHB) is one of the main fungal diseases of barley. FHB is caused by a species complex of Fusaria, of which Fusarium graminearum Schwabe is [...] Read more.
Barley is the third most important cereal crop in terms of production in Canada, and Fusarium head blight (FHB) is one of the main fungal diseases of barley. FHB is caused by a species complex of Fusaria, of which Fusarium graminearum Schwabe is the main causal species of FHB epidemics in Canada. Field surveys show that two or more Fusarium species often co-exist within the same field or grain sample, and F. poae is reported as another important species in barley. This study aimed to determine the pathogenicity of F. graminearum, F. poae, and a co-inoculation of both species causing FHB in barley. Two susceptible barley cultivars were spray-inoculated at 10 to 14 days after heading. Phenotypic disease severity was rated on a scale of 0–9 at 4, 7, 14, 21, and 28 days after inoculation. There was a significant difference in FHB severity between F. graminearum and F. poae, where infection with F. graminearum produced more severe disease ratings. F. poae generated lower disease ratings and was not statistically different from the control. When heads were co-inoculated with both Fusarium species, the resulting FHB severity was unchanged relative to heads inoculated with F. graminearum only. The ratio of F. graminearum to F. poae genomic DNA was also no different than when heads were inoculated with F. graminearum alone, as quantified with ddPCR using markers specific to each species. The metabolomic analysis of sample extracts showed that F. graminearum-associated metabolites dominated the mycotoxin profile of co-inoculated samples, which corroborated our other findings where F. graminearum appeared to outcompete F. poae in barley. No significant effect on visual FHB disease ratings or fungal DNA detection was observed between the cultivars tested. However, there were some metabolome differences between cultivars in response to the challenge by both F. graminearum and F. poae. Full article
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<p>Mean observed Fusarium head blight (FHB) disease severity on Stander and CDC Bold barley cultivars in the growth chamber experiment. The disease was assessed as described by Xue et al. [<a href="#B25-jof-10-00670" class="html-bibr">25</a>].</p>
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<p>Gene expression of the <span class="html-italic">Fusarium</span> secondary metabolite biosynthetic genes <span class="html-italic">GRA1</span> and <span class="html-italic">APS1</span> in infected barley spikes. Expression was measured by ddPCR on ground tissue inoculated with single-species or co-inoculation treatments. Heads were harvested at 28 days post-inoculation (dpi). *** <span class="html-italic">p</span> &lt; 0.0001. Error bars represent standard error of mean.</p>
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<p>Results from PCA analysis of barley cultivar Stander, comparing <span class="html-italic">F. graminearum</span> and <span class="html-italic">F. poae</span> secondary metabolite mass feature associations between monoculture and co-culture pathogen challenges. (<b>A</b>) PC1-2 score plot (red dots = mock control samples; green dots = <span class="html-italic">F. graminearum</span> monoculture samples; dark-blue dots = <span class="html-italic">F. poae</span> monoculture samples; light-blue dots = <span class="html-italic">F. graminearum</span> and <span class="html-italic">F. poae</span> co-culture samples). (<b>B</b>) PC1-2 loading plot representing mass feature variables (<span class="html-italic">Fp</span> = <span class="html-italic">F. poae</span>).</p>
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<p>Heatmap demonstrating difference in scaled (4 to −4) relative abundance of mass feature intensities observed in in planta challenge experiments using Stander and CDC Bold barley cultivars (red colour reflects greater abundance; black reflects low abundance; green reflects absence). Protonated pseudomolecular ion ([M + H]<sup>+</sup>) mass features are used to represent the various metabolites, unless otherwise specified.</p>
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