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13 pages, 2360 KiB  
Article
Selecting South American Popcorn Germplasm for Bipolaris maydis Resistance at Contrasting Nitrogen Levels
by Yure P. Souza, Gabriel M. B. Gonçalves, Julio C. G. Saluci, Rafael N. Almeida, Juliana S. Santos, Hércules S. Pereira, Rysley F. Souza, Ana Lucia R. Souza, Luana C. Vasconcelos, Marcelo S. Andrade, Antonio T. Amaral and Marcelo Vivas
Plants 2025, 14(3), 302; https://doi.org/10.3390/plants14030302 - 21 Jan 2025
Viewed by 146
Abstract
Nitrogen (N) availability plays a crucial role in plant development. However, studies indicate that the pathosystem of pathogenic fungi, such as Bipolaris maydis, which causes Southern Corn Leaf Blight (SCLB) in popcorn, interacts with N availability. Therefore, this study seeks to select [...] Read more.
Nitrogen (N) availability plays a crucial role in plant development. However, studies indicate that the pathosystem of pathogenic fungi, such as Bipolaris maydis, which causes Southern Corn Leaf Blight (SCLB) in popcorn, interacts with N availability. Therefore, this study seeks to select popcorn inbred lines (ILs), considering contrasting environments regarding N availability (low N—LN and optimal N—ON). For this, 90 ILs from 16 populations from tropical and temperate climates from South America were evaluated in five experiments using a randomized complete block design (three replications), with four common controls. From the tests, the level of severity of the ILs to SCLB was evaluated. Three trials showed greater severity in ON, one trial had higher severity in LN, and one trial did not show significant differences. However, the genotype x nitrogen level (GxN) interaction was always present. Of the 90 ILs, 73 showed resistance in both N levels, three only in LN, and four only in ON, while 10 were susceptible in both environments. On average, the lines were more susceptible in ON, and the observed GxN interactions indicate that there is a distinct behavior of the genotypes regarding the response to N in the soil, which reinforces the importance of selection in contrasting environments. Full article
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Figure 1

Figure 1
<p>Severity means of Southern Corn Leaf Blight (<span class="html-italic">Bipolaris maydis</span>) in popcorn inbred lines (G) evaluated at low (LN) and optimal (ON) levels of nitrogen (N). The superscript characters in the sources of variation (G, N, and interaction G × N) refer to the statistical significance of the effects in the ANOVA: ns (not significant); * (significant at 0.05 error probability); and ** (significant at 0.01 error probability). Bars followed by the same letter in the same nitrogen condition and same assay did not differ statistically from each other according to the Scott-Knott algorithm at a 5% probability.</p>
Full article ">Figure 2
<p>Number of popcorn inbred lines considered resistant (R) and susceptible (S) to <span class="html-italic">Bipolaris maydis</span> in each origin population, considering optimal nitrogen levels (ON) and low nitrogen levels (LN) in field experiments.</p>
Full article ">Figure 3
<p>Averages of Southern Corn Leaf Blight severity (caused by <span class="html-italic">Bipolaris maydis</span>) for groups of popcorn inbred lines classified as resistant (R), susceptible (S), and controls (R and S) in environments under low and optimal nitrogen fertilization levels. The bars represent confidence intervals based on <span class="html-italic">t</span>-tests at a 5% probability of error.</p>
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<p>Detached leaves of the susceptible control (L80) and resistant controls (P7, P2, and L75) in response to <span class="html-italic">Bipolaris maydis</span>.</p>
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<p>Precipitation (mm) and maximum, average, and minimum temperatures (°C) observed during the experimental period in the 2018–2019 crop year. Source: National Institute of Meteorology/Brazil (INMET).</p>
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26 pages, 5531 KiB  
Article
Integration of Transcriptomics and Metabolomics Provides Insight into the Growth-Promoting Functions of Solanum khasianum Endophyte in Medicago sativa
by Jiahui Li, Shijia Zhou, Jiaru Fang, Qihang Cai, Yanbo Yang, Zhenghai Sun, Liping Li and Weiwei Li
Agronomy 2025, 15(1), 251; https://doi.org/10.3390/agronomy15010251 (registering DOI) - 20 Jan 2025
Viewed by 203
Abstract
Medicago sativa is an important forage crop; its value is mainly concentrated in its economic aspects. The growth cycle and growth rate of M. sativa have an important influence on its economic benefits. Alternaria alstroemeriae has often been used as a pathogenic bacterium [...] Read more.
Medicago sativa is an important forage crop; its value is mainly concentrated in its economic aspects. The growth cycle and growth rate of M. sativa have an important influence on its economic benefits. Alternaria alstroemeriae has often been used as a pathogenic bacterium in previous studies, and studies on the growth-promoting effects of A. alstroemeriae are rare. This study aimed to assess the effects of A. alstroemeriae on the growth parameters of M. sativae and to investigate the molecular and metabolic mechanisms of M. sativa. M. sativa showed significant improvement in plant height, root length, fresh weight, and dry weight compared to uninoculated control plants. By integrating the results of transcriptome and metabolome analysis, A. alstroemeriae may promote plant growth by regulating genes associated with the biosynthetic pathways of flavonoids, anthocyanins, and proanthocyanidins in plants. These research findings provide a theoretical basis for future verification of the molecular response mechanisms and metabolic regulation of A. alstroemeriae-promoted plant growth. This study also provides a theoretical basis for sustainable agricultural development. Full article
(This article belongs to the Special Issue Application of Multi-Omics and Systems Biology in Crop Breeding)
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<p>Bar chart of physiological indicators. (<b>a</b>) Germinal length. (<b>b</b>) Germinal thickness. (<b>c</b>) Germinal fresh weight. (<b>d</b>) Plant length. (<b>e</b>) Root thickness. (<b>f</b>) Plant fresh weight. (<b>g</b>) Plant dry weight. (<b>a</b>–<b>c</b>): Growth parameters of <span class="html-italic">M. sativa</span> in germination experiments. (<b>d</b>–<b>g</b>): Growth parameters of <span class="html-italic">M. sativa</span> in potting experiments. Difference significance marking: All non-significant differences are marked with the same letter a until the average is met with a significant difference from it, after which the letter b is marked.</p>
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<p>GO enrichment analysis. The <span class="html-italic">X</span>-axis shows log Q-value values, the <span class="html-italic">Y</span>-axis shows pathways enriched in differentially expressed genes, and the size of the bubble is the number of enriched genes. (<b>a</b>) GCK vs. GF1; (<b>b</b>) YCK vs. YF1.</p>
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<p>KEGG enrichment analysis of differentially expressed genes. The <span class="html-italic">X</span>-axis is the logarithmic value of the Q value; the <span class="html-italic">Y</span>-axis is the KEGG enrichment pathway; and the bubble size is the number of enriched genes. (<b>a</b>) GCK vs. GF1; (<b>b</b>) YCK vs. YF1.</p>
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<p>Top 20 differential transcription factor statistics of different plant tissues. The <span class="html-italic">X</span>-axis is the name of transcription factor families; the <span class="html-italic">Y</span>-axis is the number of genes. (<b>a</b>) GCK vs. GF1; (<b>b</b>) YCK vs. YF1.</p>
Full article ">Figure 5
<p>Plot of PCA and OPLS-DA analysis in different plant tissues. (<b>a</b>,<b>b</b>) are plots of PCA: (<b>a</b>) GCK vs. GF1, (<b>b</b>)YCK vs. YF1; (<b>c</b>–<b>d</b>) are plots of OPLS-DA: (<b>c</b>) OPLS-DA: GCK vs. GF1, (<b>d</b>) OPLS-DA: YCK vs. YF1. (<b>a</b>,<b>b</b>): the <span class="html-italic">X</span>-axis indicates the first principal component score and the <span class="html-italic">Y</span>-axis indicates the second principal component score. Dots indicate samples, circles indicate 95% confidence intervals, and colors indicate different subgroups. (<b>c</b>,<b>d</b>): the <span class="html-italic">X</span>-axis indicates the first principal component score value and the <span class="html-italic">Y</span>-axis indicates the first orthogonal component score value. Dots indicate experimental samples and colors indicate different subgroups. The horizontal coordinate looks at the differences between groups and the vertical coordinate looks at the differences within groups; the more clustered the samples within groups and the more dispersed the samples between groups, the more reliable the results are.</p>
Full article ">Figure 6
<p>Differential metabolite volcano and heatmaps. (<b>a</b>,<b>b</b>) are volcano maps: (<b>a</b>) GCK vs. GF1, (<b>b</b>) YCK vs. YF1, (<b>c</b>,<b>b</b>) are heatmaps. (<b>b</b>) GCK vs. GF1, (<b>d</b>) YCK vs. YF1. (<b>a</b>,<b>b</b>): The <span class="html-italic">X</span>-axis represents the log2 of the multiplicity of the difference in the quantitative value of a metabolite between two samples; the <span class="html-italic">Y</span>-axis represents the log10 of the <span class="html-italic">p</span> value; and each point in the graph represents a metabolite. The larger the absolute value of the horizontal coordinate, the larger the difference in the expression of a metabolite between the two samples; the larger the value of the vertical coordinate, the more significant the difference in expression and the more reliable the differentially expressed metabolite obtained from the screening. The size of the dots indicates the size of the VIP value, red dots represent the upregulation of differences, blue dots represent the downregulation of differences, and gray dots indicate the metabolites that do not meet the conditions of differential screening. (<b>c</b>,<b>d</b>): the <span class="html-italic">X</span>-axis represents samples, the <span class="html-italic">Y</span>-axis represents metabolites, the clustering tree on the left is the differential metabolite clustering tree, and the top is the sample clustering tree. The gradient color indicates the magnitude of the quantitative value: the redder the color the higher the expression, the bluer the lower the expression.</p>
Full article ">Figure 7
<p>Metabolomic KEGG enrichment analysis plot. (<b>a</b>) GCK vs. GF1 (<b>b</b>) YCK vs. YF1. The <span class="html-italic">X</span>-axis is the impact value enriched into different metabolic pathways and the <span class="html-italic">Y</span>-axis is the enriched pathway. The dot size indicates the corresponding number of metabolites in the pathway. The color is related to the <span class="html-italic">p</span> value: the redder the color, the smaller the <span class="html-italic">p</span> value; the bluer the color, the larger the <span class="html-italic">p</span> value.</p>
Full article ">Figure 7 Cont.
<p>Metabolomic KEGG enrichment analysis plot. (<b>a</b>) GCK vs. GF1 (<b>b</b>) YCK vs. YF1. The <span class="html-italic">X</span>-axis is the impact value enriched into different metabolic pathways and the <span class="html-italic">Y</span>-axis is the enriched pathway. The dot size indicates the corresponding number of metabolites in the pathway. The color is related to the <span class="html-italic">p</span> value: the redder the color, the smaller the <span class="html-italic">p</span> value; the bluer the color, the larger the <span class="html-italic">p</span> value.</p>
Full article ">Figure 8
<p>Differential metabolites and differential gene correlation heatmap. (<b>a</b>) GCK vs. GF1, (<b>b</b>) YCK vs. YF1. The <span class="html-italic">X</span>-axis is the name of the metabolite and the <span class="html-italic">Y</span>-axis is the name of the gene family. The color of the squares represents the level of correlation coefficient; the darker the color, the higher the generational correlation. Red represents positive correlation, and green represents negative correlation.</p>
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<p>Fungal multiple sequence comparison chart.</p>
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<p>Phylogenetic tree of evolution.</p>
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20 pages, 7537 KiB  
Article
Diversity and Distribution of Phytophthora Species Along an Elevation Gradient in Natural and Semi-Natural Forest Ecosystems in Portugal
by Carlo Bregant, Eduardo Batista, Sandra Hilário, Benedetto Teodoro Linaldeddu and Artur Alves
Pathogens 2025, 14(1), 103; https://doi.org/10.3390/pathogens14010103 - 20 Jan 2025
Viewed by 291
Abstract
Globally, forests are constantly threatened by a plethora of disturbances of natural and anthropogenic origin, such as climate change, forest fires, urbanization, and pollution. Besides the most common stressors, during the last few years, Portuguese forests have been impacted by severe decline phenomena [...] Read more.
Globally, forests are constantly threatened by a plethora of disturbances of natural and anthropogenic origin, such as climate change, forest fires, urbanization, and pollution. Besides the most common stressors, during the last few years, Portuguese forests have been impacted by severe decline phenomena caused by invasive pathogens, many of which belong to the genus Phytophthora. The genus Phytophthora includes a large number of species that are invading forest ecosystems worldwide, chiefly as a consequence of global trade and human activities. This paper reports the results of a survey of Phytophthora diversity in natural and semi-natural forest ecosystems in Portugal along an elevation gradient. Isolations performed from 138 symptomatic plant tissues and rhizosphere samples collected from 26 plant species yielded a total of 19 Phytophthora species belonging to 6 phylogenetic clades, including P. cinnamomi (36 isolates), P. multivora (20), P. plurivora (9), P. cactorum (8), P. lacustris (8), P. pseudocryptogea (8), P. amnicola (6), P. hedraiandra (6), P. pseudosyringae (5), P. thermophila (5), P. bilorbang (4), P. inundata (4), P. asparagi (3), P. citricola (3), P. gonapodyides (3), P. rosacearum (3), P. chlamydospora (2), P. pachypleura (2), and P. syringae (1). Overall, the data obtained highlight the widespread occurrence of P. cinnamomi in natural ecosystems from sea level to mountain habitats. The results of the pathogenicity tests carried out on 2-year-old chestnut plants confirmed the key role of P. cinnamomi in the recrudescence of chestnut ink disease and the additional risk posed by P. pachypleura, P. plurivora, and P. multivora to Portuguese chestnut forests. Finally, three species, P. citricola, P. hedraiandra, and P. pachypleura, are reported for the first time in the natural ecosystems of Portugal. Full article
(This article belongs to the Special Issue Microbial Pathogenesis and Emerging Infections)
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<p>Overview of <span class="html-italic">Phytophthora</span> disease symptoms observed in coastal ecosystems (<b>a</b>–<b>e</b>), temperate (<b>f</b>–<b>j</b>), and montane forests (<b>k</b>–<b>o</b>) across Portugal: <span class="html-italic">Acacia longifolia</span> (<b>a</b>,<b>d</b>), <span class="html-italic">Pistacia lentiscus</span> (<b>b</b>), <span class="html-italic">Carpobrotus edulis</span> (<b>c</b>,<b>e</b>), <span class="html-italic">Quercus</span> spp. (<b>f</b>–<b>i</b>), <span class="html-italic">Rhododendron ponticum</span> (<b>j</b>), <span class="html-italic">Betula celtiberica</span> (<b>k</b>,<b>l</b>,<b>n</b>), <span class="html-italic">Castanea sativa</span> (<b>m</b>), and <span class="html-italic">Juniperus communis</span> (<b>o</b>). On the left, starting from the top, colony morphology of <span class="html-italic">Phytophthora amnicola</span>, <span class="html-italic">P. asparagi</span>, <span class="html-italic">P. bilorbang</span>, <span class="html-italic">P. cactorum</span>, <span class="html-italic">P. chlamydospora</span>, <span class="html-italic">P. cinnamomi</span>, <span class="html-italic">P. citricola</span>, <span class="html-italic">P. gonapodyides</span>, <span class="html-italic">P. hedraiandra</span>, <span class="html-italic">P. inundata</span>, <span class="html-italic">P. lacustris</span>, <span class="html-italic">P. multivora</span>, <span class="html-italic">P. pachypleura</span>, <span class="html-italic">P. plurivora</span>, <span class="html-italic">P. pseudocryptogea</span>, <span class="html-italic">P. pseudosyringae</span>, <span class="html-italic">P. rosacearum</span>, <span class="html-italic">P. syringae</span>, and <span class="html-italic">P. thermophila</span> after 7 days of growth at 20 °C on CA in the dark.</p>
Full article ">Figure 2
<p>Isolation frequency and distribution of the most common <span class="html-italic">Phytophthora</span> species isolated in this study.</p>
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<p>Distribution of <span class="html-italic">Phytophthora</span> species in Portugal. Red dots are occurrences for this study, black dots are from literature data, and blue dots in the background are for sampling areas.</p>
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<p><span class="html-italic">Phytophthora</span> diversity along the elevation gradient in Portugal. Data from the study and literature review.</p>
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<p>Maximum likelihood tree obtained from the internal transcribed spacer (ITS) sequences of <span class="html-italic">Phytophthora</span> species representative of the 12 clades. The tree was rooted to <span class="html-italic">Halophytophthora avicenniae</span> and <span class="html-italic">Nothophytophthora caduca</span>. Data are based on the General Time Reversible model. A discrete Gamma distribution was used to model evolutionary rate differences among sites. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Bootstrap support values in percentage (1000 replicates) are given at the nodes. Ex-type cultures are in bold, and isolates obtained in this study are in red.</p>
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<p>Mean lesion length (±standard deviation) and symptoms on 2-year-old seedlings of <span class="html-italic">Castanea sativa</span> detected after 1 month from the inoculation with <span class="html-italic">Phytophthora</span> spp. Values with the same letter do not differ significantly at <span class="html-italic">p</span> = 0.05, according to the LSD multiple range test.</p>
Full article ">
14 pages, 9150 KiB  
Article
Plant Adaptability to Improved Dredged Sediment
by Shoulong Zhang, Rixiong Mo, Haorong Shi, Yuanzhen Li, Yaoyao Zhou, Chenhao Wang and Guanlong Yu
Agriculture 2025, 15(2), 218; https://doi.org/10.3390/agriculture15020218 - 20 Jan 2025
Viewed by 197
Abstract
Traditional dredged sludge disposal methods are characterized by low resource utilization and high carbon emissions, leading to serious environmental pollution. This study used dredged sludge, composted pig manure, and sawdust as raw materials, and supplemented them with composite biological agents to prepare improved [...] Read more.
Traditional dredged sludge disposal methods are characterized by low resource utilization and high carbon emissions, leading to serious environmental pollution. This study used dredged sludge, composted pig manure, and sawdust as raw materials, and supplemented them with composite biological agents to prepare improved soil. Plant adaptability to the improved soil was comprehensively evaluated using factors such as seed germination index (GI). The alkaline nitrogen content in the improved soil increased by 78.61% compared to the dredged sludge, and the content of other nutrients such as available potassium also increased to varying degrees. Ryegrass seed GI increased by 51.06% in improved soil (IS1) compared to dredged sludge. The main dominant fungi in the improved soil (IS1) were Tausonia, Trichoderma, and Cystoflobasidium, which promote soil nutrient activation and antagonize pathogenic bacteria, making the environment more conducive to plant growth. Dredged sludge was successfully converted into planting soil. Fully utilizing the nitrogen, phosphorus, and other substances enriched in dredged sludge to provide nutrients for plant growth is an efficient method to achieve dredged sludge resource utilization. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Heavy metal content in artificial soil. * Corresponding to the amount of CPM added: 1% is the content of CPM in the corresponding planting soil. ** From stand: planting soil for greening [<a href="#B9-agriculture-15-00218" class="html-bibr">9</a>]. The content of each heavy metal is expressed in mg·kg<sup>−1</sup>.</p>
Full article ">Figure 2
<p>Effect of different planting soils on seed germination. (<b>a</b>) Germination rate. (<b>b</b>) Average root length. (<b>c</b>) Germination index. Different letters (a, b, c, d, e) above bars indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05) based on Duncan’s multiple range test.</p>
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<p>Venn diagram of planting soil fungal OTU distribution.</p>
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<p>Relative abundance and composition of fungal community of different planting soils at the phylum level.</p>
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<p>Community heatmap of planting soil and fungal at genus level.</p>
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<p>Heatmap of correlations between fungal genera and soil physicochemical properties. * 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05; ** 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01. The <span class="html-italic">p</span> indicates statistical significance, with smaller values representing stronger evidence of correlation.</p>
Full article ">Figure 7
<p>Bubble plot of fungal genus-level correlation with seed germination parameters. The size of bubbles is proportional to the absolute value of correlations. The genera were selected from the five most abundant taxa in the IS1, Ctrl2, and Ctrl3 groups. Pa_G: Germination rate of pak choi; Pa_L: average root length of pak choi; Pa_GI: germination index of pak choi; Ry_G: germination rate of ryegrass; Ry_L: average root length of ryegrass; Ry_GI: germination index of ryegrass; Be_G: germination rate of bermudagrass; Be_L: average root length of bermudagrass; Be_GI: germination index of bermudagrass.</p>
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20 pages, 3668 KiB  
Article
Arabidopsis Calcium Dependent Protein Kinase 3, and Its Orthologues OsCPK1, OsCPK15, and AcCPK16, Are Involved in Biotic and Abiotic Stresses
by Gardette R. Valmonte-Cortes, Colleen M. Higgins and Robin M. MacDiarmid
Plants 2025, 14(2), 294; https://doi.org/10.3390/plants14020294 - 20 Jan 2025
Viewed by 243
Abstract
Calcium-dependent protein kinases (CPKs) are plant proteins that directly bind calcium ions before phosphorylating substrates involved in biotic and abiotic stress responses, as well as development. Arabidopsis thaliana CPK3 (AtCPK3) is involved with plant signaling pathways such as stomatal movement regulation, [...] Read more.
Calcium-dependent protein kinases (CPKs) are plant proteins that directly bind calcium ions before phosphorylating substrates involved in biotic and abiotic stress responses, as well as development. Arabidopsis thaliana CPK3 (AtCPK3) is involved with plant signaling pathways such as stomatal movement regulation, salt stress response, apoptosis, seed germination and pathogen defense. In this study, AtCPK3 and its orthologues in relatively distant plant species such as rice (Oryza sativa, monocot) and kiwifruit (Actinidia chinensis, asterid eudicot) were analyzed in response to drought, bacteria, fungi, and virus infections. Two orthologues were studied in O. sativa, namely OsCPK1 and OsCPK15, while one orthologue—AcCPK16—was identified in A. chinensis. Reverse-transcriptase quantitative PCR (RT-qPCR) analysis revealed that OsCPK1 and AcCPK16 exhibit similar responses to stressors to AtCPK3. OsCPK15 responded differently, particularly in bacterial and fungal infections. An increase in expression was consistently observed among AtCPK3 and its orthologues in response to virus infection. Overexpression mutants in both Arabidopsis and kiwifruit showed slight tolerance to drought, while knockout mutants were slightly more susceptible or had little difference with wild-type plants. Overexpression mutants in Arabidopsis showed slight tolerance to virus infection. These findings highlight the importance of AtCPK3 and its orthologues in drought and pathogen responses and suggest such function must be conserved in its orthologues in a wide range of plants. Full article
(This article belongs to the Special Issue Abiotic and Biotic Stress of the Crops and Horticultural Plants)
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<p>Phylogenetic analysis and sequence alignment of Arabidopsis (<span class="html-italic">Arabidopsis thaliana</span>) CPK3 (<span class="html-italic">AtCPK3)</span> and its orthologues in rice (<span class="html-italic">Oryza sativa</span>) and kiwifruit (<span class="html-italic">Actinidia chinensis</span>). (<b>a</b>) Phylogenetic analysis of Arabidopsis, rice, and kiwifruit CPKs. Group IIb CPKs are highlighted in green. (<b>b</b>) Multiple alignments of Group IIb CPK amino acid sequence. Positions with amino acid similarities are highlighted with the same color.</p>
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<p>Transcriptional expression levels of <span class="html-italic">AtCPK3</span> and its orthologues <span class="html-italic">OsCPK1</span>, <span class="html-italic">OsCPK15</span> and <span class="html-italic">AcCPK16</span> in response to biotic and abiotic stress by RT-qPCR. (<b>a</b>) Drought (<b>b</b>) fungus (<b>c</b>) bacteria (<b>d</b>) virus. d, days; dpi, days post inoculation; Pto DC3000, <span class="html-italic">Pseudomonas syringae</span> pv<span class="html-italic">. tomato</span> DC3000<span class="html-italic">;</span> Pss, <span class="html-italic">P. syringae</span> pv<span class="html-italic">. syringae;</span> TMV, tobacco mosaic virus; TYMV, turnip yellow mosaic virus; CymMV, cymbidium mosaic virus; CMV, cucumber mosaic virus. Red broken lines indicate expression level in negative control or mock-inoculated plants, normalized at 1. Error bars indicate standard error values after normalization, log transformation and mean centering, using <span class="html-italic">AtSAND</span>, <span class="html-italic">OsEP1</span>, and <span class="html-italic">AcACTIN</span> as reference genes, respectively. Statistical support is indicated as strong (***, <span class="html-italic">p</span> ≤ 0.01), good (**, 0.01 &lt; <span class="html-italic">p</span> &lt; ~0.05) or weak (*, 0.05 &lt; <span class="html-italic">p</span> &lt; ~0.10) as per ANOVA test followed by Fisher’s LSD and/or Tukey test.</p>
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<p>Phenotype analysis of the wild-type and transgenic lines of Arabidopsis (<span class="html-italic">Arabidopsis thaliana</span>) and kiwifruit (<span class="html-italic">Actinidia chinensis</span>) under drought stress (non-watering). (<b>a</b>) Primary inflorescence height of Arabidopsis lines at 0, 7 and 14 dpi. (<b>b</b>) Dry weights of Arabidopsis lines; samples dried after 14 days of non-watering. (<b>c</b>) The drought severity score among Arabidopsis lines is shown with a scoring reference at the bottom. (<b>d</b>) Height of kiwifruit lines at 0, 7 and 14 dpi. (<b>e</b>) Dry weights of kiwifruit lines; samples dried after 14 days of non-watering. (<b>f</b>) The drought severity score among kiwifruit lines is shown with a scoring reference at the bottom. OX, overexpression mutant lines; KO, knockout mutant lines. The color of the bars matches the plant lines. For (<b>a</b>–<b>c</b>): purple, Wild-type Arabidopsis (<span class="html-italic">col-0</span>); blue, <span class="html-italic">AtCPK3</span> OX (SAIL-120-H09); green, <span class="html-italic">AtCPK3</span> OX (pHex2AtCPK3.3); red, <span class="html-italic">atcpk3-1</span> KO (SALK_106720C); and orange, <span class="html-italic">atcpk3-2</span> KO (SALK_022862); For (<b>d</b>–<b>f</b>): purple, Wild-type kiwifruit; blue, <span class="html-italic">AcCPK16</span> OX E05; green, <span class="html-italic">AcCPK16</span> OX E06; dark blue, <span class="html-italic">AcCPK16</span> OX E07; red, <span class="html-italic">AcCPK16</span> KO E05; orange, <span class="html-italic">AcCPK16</span> KO E10; and pink, <span class="html-italic">AcCPK16</span> KO E11. Bars with lighter colors indicate control plants, while bars with darker colors indicate plants subjected to non-watering. Yellow dots indicate a marked difference between the transgenic line and the wild-type plants. Statistical support is indicated as strong (***, <span class="html-italic">p</span> ≤ 0.01), good (**, 0.01 &lt; <span class="html-italic">p</span> &lt; ~0.05) or weak (*, 0.05 &lt; <span class="html-italic">p</span> &lt; ~0.10) as per ANOVA test followed by Fisher’s LSD and/or Tukey test.</p>
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<p>Phenotype analysis of the wild-type and transgenic lines of Arabidopsis (<span class="html-italic">Arabidopsis thaliana</span>) virus infection (TYMV). (<b>a</b>) Primary inflorescence height of Arabidopsis lines at 0, 7, 14, 21 and 28 dpi. (<b>b</b>) Dry weights of Arabidopsis lines; samples dried after 28 days of inoculation. (<b>c</b>) Number of siliques of Arabidopsis lines at 21 and 28 dpi. (<b>d</b>) Virus symptom severity, with scoring reference below. OX, overexpression mutant lines; KO, knockout mutant lines. Color of bars match the plant lines: purple, Wild-type Arabidopsis (<span class="html-italic">col-0</span>); blue, <span class="html-italic">AtCPK3</span> OX (SAIL-120-H09); green, <span class="html-italic">AtCPK3</span> OX (pHex2AtCPK3.3); red, <span class="html-italic">atcpk3-1</span> KO (SALK_106720C); and orange, <span class="html-italic">atcpk3-2</span> KO (SALK_022862). Bars with lighter colors indicate mock-inoculated plants, while bars with darker colors indicate plants infected with TYMV. Yellow dots indicate a marked difference between the transgenic line and the wild-type plants. Statistical support is indicated as strong, good (**, 0.01 &lt; <span class="html-italic">p</span> &lt; ~0.05) or weak (*, 0.05 &lt; <span class="html-italic">p</span> &lt; ~0.10) as per ANOVA test followed by Fisher’s LSD and/or Tukey test.</p>
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24 pages, 5932 KiB  
Article
Evaluation of Cinnamon Essential Oil and Its Emulsion on Biofilm-Associated Components of Acinetobacter baumannii Clinical Strains
by Tea Ganić, Ilinka Pećinar, Biljana Nikolić, Dušan Kekić, Nina Tomić, Stefana Cvetković, Stefana Vuletić and Dragana Mitić-Ćulafić
Antibiotics 2025, 14(1), 106; https://doi.org/10.3390/antibiotics14010106 - 19 Jan 2025
Viewed by 527
Abstract
Background/Objectives: Acinetobacter baumannii, one of the most dangerous pathogens, is able to form biofilm structures and aggravate its treatment. For that reason, new antibiofilm agents are in need, and new sources of antibiofilm compounds are being sought from plants and their products. [...] Read more.
Background/Objectives: Acinetobacter baumannii, one of the most dangerous pathogens, is able to form biofilm structures and aggravate its treatment. For that reason, new antibiofilm agents are in need, and new sources of antibiofilm compounds are being sought from plants and their products. Cinnamon essential oil is associated with a wide spectrum of biological activities, but with a further improvement of its physicochemical properties it could provide even better bioavailability. The aim of this work was the evaluation of the antibiofilm properties of cinnamon essential oil and its emulsion. Methods: In order to evaluate the antibiofilm activity, crystal violet assay was performed to determine biofilm biomass. The main components of the biofilm matrix were measured as well as the motile capacity of the tested strains. Gene expression was monitored with RT-qPCR, while treated biofilms were observed with Raman spectroscopy. Results: A particularly strong potential against pre-formed biofilm with a decreased biomass of up to 66% was found. The effect was monitored not only with regard to the whole biofilm biomass, but also on the individual components of the biofilm matrix such as exopolysaccharides, proteins, and eDNA molecules. Protein share drops in treated biofilms demonstrated the most consistency among strains and rose to 75%. The changes in strain motility and gene expressions were investigated after the treatments were carried out. Raman spectroscopy revealed the influence of the studied compounds on chemical bond types and the components present in the biofilm matrix of the tested strains. Conclusions: The results obtained from this research are promising regarding cinnamon essential oil and its emulsion as potential antibiofilm agents, so further investigation of their activity is encouraged for their potential use in biomedical applications. Full article
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<p>Antibiofilm activity of CEO and EM against <span class="html-italic">A. baumannii</span> clinical isolates GN 189 (<b>A</b>,<b>B</b>), GN 242 (<b>C</b>,<b>D</b>), and GN 1105 (<b>E</b>,<b>F</b>). Bolded values represent MIC concentrations. Statistical significance was estimated according to the negative control for EM or DMSO for CEO using one-way ANOVA, Dunnet’s post hoc test. The threshold was estimated to be * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The effect of CEO and EM on the already-formed biofilm of <span class="html-italic">A. baumannii</span> clinical isolates GN 189 (<b>A</b>,<b>B</b>), GN 242 (<b>C</b>,<b>D</b>), and GN 1105 (<b>E</b>,<b>F</b>). Bolded values represent MIC concentrations. Statistical significance was estimated according to the negative control for EM or DMSO for CEO using one-way ANOVA, Dunnet’s post hoc test. The threshold was estimated to be * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Total share of exopolysaccharides isolated from the biofilm matrix of <span class="html-italic">A. baumannii</span> clinical isolates GN 189 (<b>A</b>), GN 242 (<b>B</b>), and GN 1105 (<b>C</b>). The tested concentrations of CEO and EM are the MIC values of GN 189 (0.5 mg/mL for CEO and 0.125 mg/mL for EM), GN 242 (0.25 mg/mL for CEO and 0.125 mg/mL for EM), and GN 1105 (1 mg/mL for CEO and 0.125 mg/mL for EM).The statistical significance was estimated according to the negative control for EM or DMSO for CEO, and by using one-way ANOVA, Dunnet’s post hoc test. The statistically significant threshold was * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Total share of proteins extracted from biofilm matrix <span class="html-italic">A. baumannii</span> clinical isolates GN 189 (<b>A</b>), GN 242 (<b>B</b>), and GN 1105 (<b>C</b>). The tested concentrations of CEO and EM are the MIC values of GN 189 (0.5 mg/mL for CEO and 0.125 mg/mL for EM), GN 242 (0.25 for CEO and 0.125 mg/mL), and GN 1105 (1 mg/mL for CEO and 0.125 mg/mL). Statistical significance was defined by comparing treatments with the negative control for EM or DMSO for CEO, using one-way ANOVA, Dunnet’s post hoc test. The threshold was estimated to be * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The effects of CEO and EM on the motility of strains GN 189 (<b>A</b>), GN 242 (<b>B</b>), and GN 1105 (<b>C</b>). The tested concentrations of CEO and EM are the MIC of GN 189 (0.25 mg/mL for CEO and 0.062 mg/mL for EM), GN 242 (0.125 for CEO and 0.062 mg/mL), and GN 1105 (0.5 mg/mL for CEO and 0.062 mg/mL). Statistical significance was determined according to the negative control for EM or DMSO for CEO, performed using one-way Anova, Dunnet’s post hoc test, while the threshold was * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Gene expression analysis of the <span class="html-italic">A. baumannii</span> biofilm of clinical isolates GN 189 (<b>A</b>), GN 242 (<b>B</b>), and GN 1105 (<b>C</b>), treated with CEO and EM. The tested concentrations of CEO and EM are the MIC values of GN 189 (0.5 mg/mL for CEO and 0.125 mg/mL for EM), GN 242 (0.25 for CEO and 0.125 mg/mL), and GN 1105 (1 mg/mL for CEO and 0.125 mg/mL). The results are presented as the normalized gene expression. Statistical significance regarding the negative control (C in graphs) for EM and DMSO for CEO was determined using one-way ANOVA with Dunnet’s post hoc test and the threshold was * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Averages of normalized Raman spectra of the <span class="html-italic">A. baumannii</span> biofilm matrix: GN 189 (<b>A</b>), GN 242 (<b>B</b>), GN 1105 (<b>C</b>); (C—control, CEO–cinnamon essential oil, EM—cinnamon emulsion). Spectral range is from 350 to 1800 cm<sup>−1</sup>.</p>
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<p>PC analysis of score plots (<b>A</b>) and loading plots (<b>B</b>,<b>C</b>) obtained from the Raman spectra of the <span class="html-italic">A.baumannii</span> GN 189 biofilm.</p>
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<p>PC analysis score plots (<b>A</b>) and loading plots (<b>B</b>,<b>C</b>) obtained from the Raman spectra of the <span class="html-italic">A.baumannii</span> GN 242 biofilm.</p>
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<p>PC analysis score plots (<b>A</b>) and loading plots (<b>B</b>,<b>C</b>) obtained from the Raman spectra of the <span class="html-italic">A.baumannii</span> GN 1105 biofilm.</p>
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22 pages, 3962 KiB  
Review
Compounds Involved in the Invasive Characteristics of Lantana camara
by Hisashi Kato-Noguchi and Midori Kato
Molecules 2025, 30(2), 411; https://doi.org/10.3390/molecules30020411 - 19 Jan 2025
Viewed by 225
Abstract
Lantana camara L. is native to tropical America and has naturalized in many other tropical, subtropical, and temperate regions in Asia, Africa, Oceania, North and South America, and Europe. L. camara infests diverse habitats with a wide range of climatic factors, and its [...] Read more.
Lantana camara L. is native to tropical America and has naturalized in many other tropical, subtropical, and temperate regions in Asia, Africa, Oceania, North and South America, and Europe. L. camara infests diverse habitats with a wide range of climatic factors, and its population increases aggressively as one of the world’s 100 worst invasive alien species. Its infestation reduces species diversity and abundance in the natural ecosystems and reduces agricultural production. The life history characteristics of L. camara, such as its high reproductive ability and high adaptive ability to various environmental conditions, may contribute to its ability to infest and increase its population. Possible evidence of the compounds involved in the defense functions of L. camara against natural enemies, such as herbivore mammals and insects, parasitic nematodes, pathogenic fungi and bacteria, and the allelochemicals involved in its allelopathy against neighboring competitive plant species, have accumulated in the literature over three decades. Lantadenes A and B, oleanonic acid, and icterogenin are highly toxic to herbivore mammals, and β-humulene, isoledene, α-copaene thymol, and hexadecanoic acid have high insecticidal activity. β-Caryophyllene and cis-3-hexen-1-ol may function as herbivore-induced plant volatiles which are involved in sending warning signals to undamaged tissues and the next plants of the same species. Farnesol and farnesal may interrupt insect juvenile hormone biosynthesis and cause abnormal metamorphosis of insects. Several triterpenes, such as lantanolic acid, lantoic acid, pomolic acid, camarin, lantacin, camarinin, ursolic acid, and oleanonic acid, have demonstrated nematocidal activity. Lantadene A, β-caryophyllene, germacrene-D, β-curcumene, eicosapentaenoic acid, and loliolide may possess antimicrobial activity. Allelochemicals, such as caffeic acid, ferulic acid, salicylic acid, α-resorcylic acid, p-hydroxybenzoic acid, vanillic acid, unbelliferone, and quercetin, including lantadenes A and B and β-caryophyllene, suppress the germination and growth of neighboring plant species. These compounds may be involved in the defense functions and allelopathy and may contribute to L. camara’s ability to infest and to expand its population as an invasive plant species in new habitats. This is the first review to focus on how compounds enhance the invasive characteristics of L. camara. Full article
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Graphical abstract

Graphical abstract
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<p>Stand and leaves of <span class="html-italic">L. camara</span>.</p>
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<p>Flowers and fruits of <span class="html-italic">L. camara</span>.</p>
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<p>The compounds involved in the defense function against herbivore mammals.</p>
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<p>The compounds involved in the defense function against herbivorous insects.</p>
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<p>The compounds involved in the defense function against parasitic nematodes.</p>
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<p>The compounds involved in the defense function against pathogenic fungi and bacteria.</p>
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<p>The compounds involved in the allelopathy.</p>
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<p>Action mechanisms of the compounds discussed in this paper. These compounds are involved in the hepathoxic, insecticidal, nematocidal, fungicidal, and allelopathic activity of <span class="html-italic">L. camara</span>. Purple arrow: direct action; blue arrow: secondary and tertiary action.</p>
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14 pages, 683 KiB  
Article
Production of an Extract with β-1,4-Xylanase Activity by Fusarium oxysporum f. sp. melonis on a Sonicated Brewer’s Spent Grain Substrate
by Irma A. Arreola-Cruz, Rosalba Troncoso-Rojas, Francisco Vásquez-Lara, Nina G. Heredia-Sandoval and Alma R. Islas-Rubio
Fermentation 2025, 11(1), 42; https://doi.org/10.3390/fermentation11010042 - 18 Jan 2025
Viewed by 493
Abstract
The Fusarium oxysporum species commonly found in soil include plant and human pathogens, and nonpathogenic species. F. oxysporum grown on lignocellulosic substrates under submerged conditions produces an extracellular enzyme profile with hemicellulolytic and cellulolytic activities. Our aim was to produce an extract of [...] Read more.
The Fusarium oxysporum species commonly found in soil include plant and human pathogens, and nonpathogenic species. F. oxysporum grown on lignocellulosic substrates under submerged conditions produces an extracellular enzyme profile with hemicellulolytic and cellulolytic activities. Our aim was to produce an extract of Fusarium oxysporum f. sp. melonis with β-1,4-xylanase activity after fermentation on a Brewers’ spent grain (BSG)-containing substrate. We prepared the BSG substrate, with or without sonication, for the submerged fermentation of Fusarium oxysporum previously isolated from local soil and preserved at 4 °C. First, an enriched inoculum was prepared, and later, the production of β-1,4-xylanase using the BSG substrates was monitored for up to 6 or 10 days in the enriched inoculum or in the enzyme extract, respectively. An activity of β-1,4-xylanase 12.0 U/mL (day 3) was obtained in the enriched inoculum with the untreated BSG, remaining constant for 3 days. A significant increase in the activity of this enzyme was observed (day 6), especially in the extract obtained using the sonicated BSG substrate (39 U/mL). Applying ultrasound to the BSG before its use in a submerged fermentation with Fusarium oxysporum f. sp. melonis could be an alternative for producing β-1,4-xylanase. Full article
(This article belongs to the Section Microbial Metabolism, Physiology & Genetics)
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<p>Monitoring of β-1,4-xylanase (enriched inoculum) activity in untreated BSG and sonicated BSG at 30 °C and 150 rpm. Values (mean ± standard deviation) with different superscript letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Monitoring of β-1,4-xylanase (enzyme extract production) activity in untreated BSG and ultrasound-treated BSG at 30 °C and 150 rpm. Values (mean ± standard deviation) with different superscript letters are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 4472 KiB  
Article
Antifungal and Antibacterial Activity of Aqueous and Ethanolic Extracts of Different Rosa rugosa Parts
by Žaneta Maželienė, Jolita Kirvaitienė, Kamilė Kaklauskienė, Rasa Volskienė and Asta Aleksandravičienė
Microbiol. Res. 2025, 16(1), 26; https://doi.org/10.3390/microbiolres16010026 - 18 Jan 2025
Viewed by 283
Abstract
With the rising incidence of drug-resistant pathogens, focus should be placed on biologically active compounds derived from plant species used in herbal medicine, as these compounds may provide a new source of antifungal and antibacterial activities. The aim of this study was to [...] Read more.
With the rising incidence of drug-resistant pathogens, focus should be placed on biologically active compounds derived from plant species used in herbal medicine, as these compounds may provide a new source of antifungal and antibacterial activities. The aim of this study was to evaluate the antifungal and antibacterial activity of ethanol and aqueous extracts from different parts of Rosa rugosa. In order to evaluate the antimicrobial activity of the extracts of R. rugosa rose hips, flowers, petals, leaves, stems, and roots, a laboratory microbiological test was performed using the well diffusion method in agar. A rotary evaporator was used for extract concentration and extractant removal. Antimicrobial activity was tested against one fungus, four Gram-positive, and four Gram-negative bacteria. The leaf extracts exhibited the strongest antimicrobial activity, followed by the extracts from the petals and rose hips, while weaker activity was observed in the root extracts. The extracts from the stems and rose hips showed the weakest effect. Ethanol extracts were more effective than water extracts. Aqueous and ethanolic extracts of R. rugosa parts demonstrated antifungal activity against Candida albicans, with ethanol extracts proving to be more effective. Among all the R. rugosa parts analyzed, the petals exhibited the strongest antifungal activity. Full article
(This article belongs to the Special Issue Antifungal Activities of Plant Extracts)
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<p>Sampling locations of <span class="html-italic">R rugosa</span> in Lithuania are marked in the red circle.</p>
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<p>Antifungal activity of aqueous and ethanolic extracts of different parts of <span class="html-italic">R</span>. <span class="html-italic">rugosa</span> against <span class="html-italic">C</span>. <span class="html-italic">albicans</span> expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> hips against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> flowers against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> petals against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> leaves against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> roots against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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<p>Antimicrobial activity of aqueous and ethanolic extracts of <span class="html-italic">R. rugosa</span> stems against Gram-positive and Gram-negative microorganisms expressed as the three attempts average diameter of inhibition zones, mm. The negative control (10% DMSO) showed no inhibition zone.</p>
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17 pages, 1448 KiB  
Article
Transcriptome of Arabidopsis thaliana Plants Exposed to Human Parasites Cryptosporidium parvum and Giardia lamblia
by Yaroslav Ilnytskyy, Andrey Golubov, Boseon Byeon and Igor Kovalchuk
Int. J. Plant Biol. 2025, 16(1), 13; https://doi.org/10.3390/ijpb16010013 - 18 Jan 2025
Viewed by 357
Abstract
Pathogen infection in animals and plants is recognized in a relatively similar manner by the interaction of pattern recognition receptors on the host cell surface with pathogen-associated molecular patterns on the pathogen surface. Previous work demonstrates that animal pathogenic bacteria can be recognized [...] Read more.
Pathogen infection in animals and plants is recognized in a relatively similar manner by the interaction of pattern recognition receptors on the host cell surface with pathogen-associated molecular patterns on the pathogen surface. Previous work demonstrates that animal pathogenic bacteria can be recognized by plant receptors and alter transcriptome. In this work, we have hypothesized that exposure to human parasites, Cryptosporidium parvum and Giardia lamblia, would also trigger pathogen response in plants, leading to changes in transcriptome. Detached Arabidopsis leaves were exposed for one hour to heat-inactivated Cryptosporidia or Giardia. The transcriptome profile showed large changes in gene expression with significant overlap between two parasites, including upregulated GO terms “cellular response to chitin”, “response to wounding”, “response to oomycetes”, “defense response to fungus”, “incompatible interaction”, and “activation of innate immune response”, and downregulated GO terms “positive regulation of development”, “cell surface”, “regulation of organ growth”, “wax biosynthetic process”, “leaf and shoot morphogenesis”. Uniquely downregulated GO terms in response to Cryptosporidia were GO terms related to chromatin remodelling, something that was not reported before. To conclude, it appears that while Cryptosporidia or Giardia are not pathogens of Arabidopsis, this plant possesses various mechanisms of recognition of pathogenic components of parasites. Full article
(This article belongs to the Section Plant–Microorganisms Interactions)
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<p>(<b>A</b>). Heatmap showing Euclidean distances between samples calculated with variance. transformed data. Samples were clustered with hclust() function with default settings. (<b>B</b>). Heatmap of top 1,000 DEGs obtained using DESeq.</p>
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<p>Volcano plot visualization of DEGs using DESeq and NOISeq methods. Y axis shows log2 fold difference between treatment and control. X axis shows the mean expression level of genes. Red dots show significantly differentially expressed genes.</p>
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<p>Overlap between DEGs. (<b>A</b>). Overlap between DEGs found by DESeq and NOISeq methods for Cryptosporidia. (<b>B</b>). Overlap between DEGs found by DESeq and NOISeq methods for Giardia. (<b>C</b>). Overlap between DEGs in Cryptosporidia and Giardia found using DESeq method. (<b>D</b>). Overlap between DEGs in Cryptosporidia and Giardia found using NOISeq method.</p>
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22 pages, 2243 KiB  
Review
Defensive Mechanisms of Mikania micrantha Likely Enhance Its Invasiveness as One of the World’s Worst Alien Species
by David R. Clements and Hisashi Kato-Noguchi
Plants 2025, 14(2), 269; https://doi.org/10.3390/plants14020269 - 18 Jan 2025
Viewed by 448
Abstract
Mikania micrantha Kunth is native to tropical America and has invaded tropical and subtropical Asia and numerous Pacific Islands. It forms dense thickets and reduces native species diversity and populations in its introduced range. This invasive vine also seriously impacts many agricultural crops [...] Read more.
Mikania micrantha Kunth is native to tropical America and has invaded tropical and subtropical Asia and numerous Pacific Islands. It forms dense thickets and reduces native species diversity and populations in its introduced range. This invasive vine also seriously impacts many agricultural crops and is listed as one of the world’s 100 worst invasive alien species. Its life history characteristics, such as the production of large numbers of wind-dispersed seeds, vegetative reproduction, rapid growth, and genetic diversity all contribute to its invasiveness. In this review, we focus on how mechanisms to defend against its natural enemies boost the invasiveness of M. micrantha. It possesses potent defenses against natural enemies such as pathogenic fungi, herbivorous insects, and parasitic nematodes, and exhibits allelopathic potential against plant competitors. These defensive abilities, in concert with its formidable life history characteristics, contribute to the invasiveness of M. micrantha, potentially leading to further naturalization. Several other reviews have summarized the biology and management of the species, but ours is the first review to focus on how the defensive mechanisms of M. micrantha likely enhance its invasiveness. Relatively little is known about the array of defensive capabilities of M. micrantha; therefore, there is considerable scope for further research on its chemical defenses. Full article
(This article belongs to the Special Issue Plant Invasions across Scales)
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<p>Plant anatomy of <span class="html-italic">Mikania micrantha</span>. (<b>A</b>) flowering cluster showing distinctive 4.5–60.0 mm white flowers, (<b>B</b>) seeds with pappuses (1.5 mm long), (<b>C</b>) adventitious roots growing from a vine (with Joseph DiTomaso, University of California), (<b>D</b>) heart-shaped leaf structure (4 to 13 cm in length). Photos by David R. Clements taken in Yunnan Province, China.</p>
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<p>Infestations of <span class="html-italic">Mikania micrantha</span> in Yunnan Province, China. (<b>A</b>) Forest being overtaken by <span class="html-italic">M. micrantha</span> vines, demonstrating the ability of the vines to attain heights of 10 m or more, (<b>B</b>) <span class="html-italic">M. micrantha</span> in flower, (<b>C</b>) <span class="html-italic">M. micrantha</span> in a riparian zone on a river, showing potential to spread. (<b>D</b>) Trees being smothered by <span class="html-italic">M. micrantha</span> vines. Photos by David R. Clements.</p>
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<p>Compounds involved in anti-fungal activity of <span class="html-italic">M. micrantha.</span> 1: mikanolide, 2: dihydromikanolide, 3: deoxymikanolide, 4: scandenolide, 5: dihydroscandenolide.</p>
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<p>Compounds involved in anti-insect activity of <span class="html-italic">M. micrantha.</span> 6: α-terpinene, 7: limonene, 8: linalool.</p>
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<p>Compounds involved in allelopathic activity of <span class="html-italic">M. micrantha.</span> 9: 2,3-epoxy-1-hydroxy-4,9-germacradiene-12,8:15,6-diolide, 10: 8,10-dihydroxy-9-benzoyloxythymol, 11: 8,10-dihydroxy-9-(2-methylbutyryloxy)thymol, 12: β-D-glucopyranosyl-15α-(3-hydroxyl-3-methylbutanoyloxy)-ent-16-kauren-19-oate, 13: α-terpineol, 14: β-ocimene, 15: β-myrcene, 16: α-pinene, 17: β-caryophyllene, 18: benzoic acid, 19: cinnamic acid.</p>
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19 pages, 2362 KiB  
Article
Respiratory and Enteric Bacterial Pathogens in Municipal Wastewater: A Potential Risk of Infection to Workers
by Evida Poopedi, Tanusha Singh and Annancietar Gomba
Water 2025, 17(2), 268; https://doi.org/10.3390/w17020268 - 18 Jan 2025
Viewed by 374
Abstract
Investigating human pathogens in wastewater is crucial for identifying and predicting potential occupational health risks faced by wastewater treatment plant (WWTP) workers. This study aimed to determine the occurrence and levels of Legionella pneumophila, Mycobacterium spp., Arcobacter butzleri, and Aeromonas hydrophila [...] Read more.
Investigating human pathogens in wastewater is crucial for identifying and predicting potential occupational health risks faced by wastewater treatment plant (WWTP) workers. This study aimed to determine the occurrence and levels of Legionella pneumophila, Mycobacterium spp., Arcobacter butzleri, and Aeromonas hydrophila in untreated municipal wastewater. Grab influent, activated sludge, and secondary settling tank (SST) effluent samples were collected bi-weekly over 6 months from 5 WWTPs in Tshwane, South Africa. Mycobacterium spp., A. butzleri, and A. hydrophila were detected using quantitative PCR (qPCR), while Legionella was detected using both a culture method and qPCR. The four pathogens were identified in most samples at varying levels. Legionella pneumophila had a positivity rate of 92%, ranging from 2 to 5.4 log10 MPN/100 mL. Detection rates of Legionella spp., L. pneumophila, and L. pneumophila serogroup 1 were 97%, 75%, and 69%, respectively, with up to 5.3 log10 gene copies (GC)/mL. Importantly, this study demonstrates molecular typing of L. pneumophila serogroup 1 in wastewater, a topic that has been rarely documented. Mycobacterium spp. were detected in all samples at varying levels (log10 GC/mL) in influent (2.8–7.6), activated sludge (4.8–8.9), and SST effluent (3.8–8.9) samples. Arcobacter butzleri and A. hydrophila were detected in 96% and 82% of the samples, respectively, with GC levels in influent, activated sludge, and SST effluent ranging from 0.8 to 6.6, 1.5 to 6.5, and 0.7 to 6.6 log10 GC/mL for A. butzleri, and similar levels for A. hydrophila. These findings underscore the presence of respiratory and enteric pathogens at various treatment points, suggesting potential occupational exposure for WWTP workers. This emphasises the need for microbiological risk assessments (RAs) or reviewing existing RAs and implementing necessary control measures to protect WWTP workers. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Concentration of culturable <span class="html-italic">L. pneumophila</span> in wastewater at different treatment stages across five WWTPs over a six-month sampling period. The whiskers illustrate the minimum and maximum; the outer box illustrates the 1st and 3rd quartiles, and the inner line illustrates the median. Different letters on the graph indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) in means within a site, as determined by Bonferroni post hoc test following a two-way ANOVA.</p>
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<p>Gene copy levels of <span class="html-italic">Legionella</span> spp. (<b>A</b>) <span class="html-italic">L. pneumophila,</span> (<b>B</b>) and <span class="html-italic">L. pneumophila</span> sg1 (<b>C</b>) in wastewater at different treatment stages over six-month sampling period. Whiskers illustrate the minimum and maximum, the outer box illustrates the 1st and 3rd quartiles, and the inner line illustrates the median. Different letters on the graph indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) in means within a site, as determined by Bonferroni post hoc test following a two-way ANOVA.</p>
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<p>Gene copy levels of <span class="html-italic">Mycobacterium</span> spp. in wastewater at different treatment stages over a six-month sampling period. Whiskers illustrate the minimum and maximum, the outer box illustrates the 1st and 3rd quartiles, and the inner line illustrates the median. Different letters on the graph indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) in means within a site, as determined by Bonferroni post hoc test following a two-way ANOVA.</p>
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<p>Gene copy levels of <span class="html-italic">A. butzleri</span> (<b>A</b>) and <span class="html-italic">A. hydrophila</span> (<b>B</b>) in wastewater at different treatment stages over a six-month sampling period. Whiskers illustrate the minimum and maximum, the outer box illustrates the 1st and 3rd quartiles, and the inner line illustrates the median. Different letters on the graph indicate significant differences (<span class="html-italic">p</span> ≤ 0.05) in means within a site, as determined by Bonferroni post hoc test following a two-way ANOVA.</p>
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40 pages, 2452 KiB  
Review
Groundbreaking Technologies and the Biocontrol of Fungal Vascular Plant Pathogens
by Carmen Gómez-Lama Cabanás and Jesús Mercado-Blanco
J. Fungi 2025, 11(1), 77; https://doi.org/10.3390/jof11010077 - 18 Jan 2025
Viewed by 294
Abstract
This review delves into innovative technologies to improve the control of vascular fungal plant pathogens. It also briefly summarizes traditional biocontrol approaches to manage them, addressing their limitations and emphasizing the need to develop more sustainable and precise solutions. Powerful tools such as [...] Read more.
This review delves into innovative technologies to improve the control of vascular fungal plant pathogens. It also briefly summarizes traditional biocontrol approaches to manage them, addressing their limitations and emphasizing the need to develop more sustainable and precise solutions. Powerful tools such as next-generation sequencing, meta-omics, and microbiome engineering allow for the targeted manipulation of microbial communities to enhance pathogen suppression. Microbiome-based approaches include the design of synthetic microbial consortia and the transplant of entire or customized soil/plant microbiomes, potentially offering more resilient and adaptable biocontrol strategies. Nanotechnology has also advanced significantly, providing methods for the targeted delivery of biological control agents (BCAs) or compounds derived from them through different nanoparticles (NPs), including bacteriogenic, mycogenic, phytogenic, phycogenic, and debris-derived ones acting as carriers. The use of biodegradable polymeric and non-polymeric eco-friendly NPs, which enable the controlled release of antifungal agents while minimizing environmental impact, is also explored. Furthermore, artificial intelligence and machine learning can revolutionize crop protection through early disease detection, the prediction of disease outbreaks, and precision in BCA treatments. Other technologies such as genome editing, RNA interference (RNAi), and functional peptides can enhance BCA efficacy against pathogenic fungi. Altogether, these technologies provide a comprehensive framework for sustainable and precise management of fungal vascular diseases, redefining pathogen biocontrol in modern agriculture. Full article
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<p>Word cloud showing the most relevant terms extracted from the titles of the articles consulted to produce this review. The frequency each term appears in the titles is visually emphasized in the cloud by their size. The figure was generated using the free online ChatGPT (<a href="https://chatgpt.com/" target="_blank">https://chatgpt.com/</a>, accessed on 12 December 2024).</p>
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<p>Two examples of fungal vascular diseases affecting highly relevant crops. (<b>A</b>) Banana orchard in Tenerife island affected by Fusarium wilt (<span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">cubense</span>) (photo credit Javier López Cepero); (<b>B</b>) Olive trees in Southern Spain showing Verticillium wilt (<span class="html-italic">Verticillium dahliae</span>) symptoms (photo credit Jesús Mercado-Blanco).</p>
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<p>A graphical overview of the cutting-edge technologies mentioned in this review and aimed at improving biocontrol strategies for vascular fungal diseases. The figure was created using icons and templates from the free online BioRender (<a href="https://www.biorender.com/" target="_blank">https://www.biorender.com/</a>, accessed on 12 December 2024), except for the digital twins and microbiome transplant images, which were generated with the free online version of ChatGPT (<a href="https://chatgpt.com/" target="_blank">https://chatgpt.com/</a>, accessed on 12 December 2024). The acronyms used are defined as follows: biological control agent (BCA), artificial intelligence (AI), and RNA interference (RNAi).</p>
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23 pages, 350 KiB  
Article
Functional and Antimicrobial Properties of Propolis from Different Areas of Romania
by Gianluca Albanese, Alexandru Ioan Giurgiu, Otilia Bobiș, Adriana Cristina Urcan, Sara Botezan, Victorița Bonta, Tudor Nicolas Ternar, Claudia Pașca, Massimo Iorizzo, Antonio De Cristofaro, Emilio Caprio and Daniel Severus Dezmirean
Appl. Sci. 2025, 15(2), 898; https://doi.org/10.3390/app15020898 (registering DOI) - 17 Jan 2025
Viewed by 381
Abstract
Propolis is a complex resinous substance produced by Apis mellifera L. through a process of mixing tree resins with saliva and beeswax. This substance plays a crucial role in the hive’s defence against a range of pathogenic agents, maintaining a consistent internal temperature [...] Read more.
Propolis is a complex resinous substance produced by Apis mellifera L. through a process of mixing tree resins with saliva and beeswax. This substance plays a crucial role in the hive’s defence against a range of pathogenic agents, maintaining a consistent internal temperature and aseptic environment for the bee colony. The objective of the present study was to assess the chemical composition and antibacterial characteristics of five hydroalcoholic propolis extracts sourced from diverse geographic regions within Romania. This study shows that the biological and functional properties of propolis extracts are related to the plant resources in the vicinity of the hives, and this relates to greater or lesser bioactivity of the extracts; therefore, to standardise the extracts, it is essential to catalogue the plant essences in the proximity of the hives. The antimicrobial activity of propolis extract from each apiary was evaluated against five Gram-positive, five Gram-negative bacteria, and one fungal strain, using the difuzimetric method and minimum inhibitory concentration (MIC). The results showed some variability, supporting the hypothesis that not only may the botanical origin influence the properties of propolis but also that a higher number of flavonoids influences the higher antimicrobial activity in the extracts. Full article
(This article belongs to the Special Issue New Advances in Antioxidant of Bee Products)
16 pages, 2084 KiB  
Article
The Exocyst Subunits EqSec5 and EqSec6 Promote Powdery Mildew Fungus Growth and Pathogenicity
by Jinyao Yin, Xuehuan Zhu, Yalong Chen, Yanyang Lv, Jiaxin Shan, Yuhan Liu, Wenbo Liu, Weiguo Miao and Xiao Li
J. Fungi 2025, 11(1), 73; https://doi.org/10.3390/jof11010073 - 17 Jan 2025
Viewed by 302
Abstract
The exocyst complex in eukaryotic cells modulates secretory vesicle transportation to promote exocytosis. The exocyst is also required for the hyphal growth and pathogenic development of several filamentous phytopathogens. Obligate biotrophic powdery mildew fungi cause considerable damage to many cash crops; however, the [...] Read more.
The exocyst complex in eukaryotic cells modulates secretory vesicle transportation to promote exocytosis. The exocyst is also required for the hyphal growth and pathogenic development of several filamentous phytopathogens. Obligate biotrophic powdery mildew fungi cause considerable damage to many cash crops; however, the exocyst’s roles in this group of fungi is not well studied. To verify the functions of the exocyst in powdery mildew fungus, we identified two exocyst subunits, EqSec5 and EqSec6, from Erysiphe quercicola, a powdery mildew fungus that infects the rubber tree Hevea brasiliensis. When GFP-fused EqSec5 and EqSec6 were introduced into E. quercicola and another phytopathogenic fungus, Magnaporthe oryzae, they primarily localized to the hyphal tip region. Inducing gene silencing of EqSec5 or EqSec6 caused growth and infection defects, and those defects could not be fully restored under the NADPH oxidase inhibitor treatment to the plant. The silenced strains also induced the host defense response including reactive oxygen species accumulation and callose deposition. The silencing of EqSec5 or EqSec6 also inhibited the secretion of the effector protein EqIsc1, interrupting plant salicylic acid biosynthesis. Yeast two-hybrid and gene overexpression assays suggested that EqSec5 and EqSec6 interact with each other and can complement each other’s function during host infection. Overall, our study provides evidence that the exocyst in this powdery mildew fungus facilitates effector secretion, hyphal growth, and infection. Full article
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<p>Subcellular localizations of EqSec5- and EqSec6-GFP in hyphae of <span class="html-italic">E. quercicola</span> and <span class="html-italic">M. oryzae</span>. (<b>A</b>) EqSec5- and EqSec6-GFP in hyphal tip region of <span class="html-italic">E. quercicola</span>. Arrows indicate the hyphal tip-localized region. FM4-64 was used to label the membrane and the Spitzenkörper. Bars: 50 μm. (<b>B</b>) Quantification of fungal cells with hyphal tip-localized GFP of <span class="html-italic">E. quercicola</span>. (<b>C</b>) EqSec5-GFP, EqSec6-GFP, MoSec5-GFP, and MoSec6-GFP in hyphal tip region of <span class="html-italic">M. oryzae</span>. Arrows indicate the hyphal tip-localized region. FM4-64 was used to label the membrane and the Spitzenkörper. Bars: 50 μm. (<b>D</b>) Quantification of fungal cells with hyphal tip-localized GFP of <span class="html-italic">M. oryzae</span>. In (<b>B</b>,<b>D</b>), the data represent means ± SE (n = 9 replicates from 3 independent experiments). The GFP stain was used as the control. Significant differences between the two data groups were analyzed using Student’s <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The silencing of <span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> inhibited the pathogenicity of <span class="html-italic">E. quercicola</span>. The gene silencing of <span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> was induced via electrotransformation and a dsRNA treatment. (<b>A</b>) Relative transcript levels of <span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> were determined using qRT-PCR. The data represent means ± SE (n = 9 replicates from 3 independent experiments). The levels of <span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> in the wild-type (WT) or H<sub>2</sub>O-treated strain were normalized to 1.0. Student’s <span class="html-italic">t</span>-test was used to analyze significant differences (** <span class="html-italic">p</span> &lt; 0.01). (<b>B</b>) Disease symptoms on <span class="html-italic">H. brasiliensis</span> leaves inoculated with <span class="html-italic">E. quercicola</span> stains. Representative images were captured at 7 dpi. (<b>C</b>) Quantification of lesion area in <span class="html-italic">H. brasiliensis</span> leaves inoculated with <span class="html-italic">E. quercicola</span> strains via electrotransformation at 7 dpi. (<b>D</b>) Quantification of lesion area in <span class="html-italic">H. brasiliensis</span> leaves inoculated with <span class="html-italic">E. quercicola</span> strains via SIGS at 7 dpi. In (<b>C</b>,<b>D</b>), significant differences are indicated by different letters according to one-way analysis of variance and Turkey’s multiple-comparison test (<span class="html-italic">p</span> &lt; 0.01), and the data represent means ± SE (n = 24 inoculated sites from 3 independent experiments). (<b>E</b>) The quantification of infection types for <span class="html-italic">E. quercicola</span> strains inoculated onto <span class="html-italic">H. brasiliensis</span> leaves at 7 dpi. The infection of each conidium was divided into three types: no penetration (type I), limited hyphal growth (colony radius &lt; 150 μm; type II), and extended hyphae (colony radius &gt; 150 μm; type III). Leaf tissues were bleached, and hyphae were stained with aniline blue. The experiments were conducted three times with similar results. Bars: 20 μm. In (<b>A</b>–<b>E</b>), <span class="html-italic">-EqSec5</span>/<span class="html-italic">6</span>—the strains transformed with <span class="html-italic">EqSec5/6</span>-silencing plasmids; GFP—the strain transformed with GFP.</p>
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<p>EqSec5 and EqSec6 regulate the secretion of effector proteins. (<b>A</b>) The localization of EqIsc1-GFP in hyphae and haustoria of <span class="html-italic">E. quercicola</span> with dsRNA treatment. Arrows indicate the haustoria. The dyes CFW and FM4-64 were used to label the cell wall and membrane. Bars: 200 μm. (<b>B</b>) The percentage of haustoria with EqIsc1-GFP localized on the periphery of cells. The GFP strain was used as the control. (<b>C</b>) Relative transcript levels of <span class="html-italic">EqIsc1</span>, <span class="html-italic">EqCSEP01276,</span> and <span class="html-italic">EqCSEP04187</span> were determined using qRT-PCR. The level of each gene in the strain with the GFP-dsRNA treatment was normalized to 1.0. (<b>D</b>) Free SA levels in <span class="html-italic">H. brasiliensis</span> leaves infected with <span class="html-italic">E. quercicola</span> strains. The GFP strain was used as the control. In (<b>B</b>–<b>D</b>), the data represent means ± SE (n = 9 inoculated sites from 3 independent experiments). Significant differences between the two data groups were analyzed using Student’s <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p><span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> silencing elicited defense responses in <span class="html-italic">H. brasiliensis</span>. (<b>A</b>) Callose deposition and ROS accumulation in infected leaves (7 dpi) were detected with aniline blue and DAB staining. Bars: 200 μm. Arrows indicate the ROS accumulation and callose deposition, respectively. (<b>B</b>) Quantification of the infected sites (7 dpi) with ROS accumulation labeled via DAB staining. The percentage of ROS staining area/0.3 mm<sup>2</sup> leaf area was calculated. (<b>C</b>) The determination of ROS levels in infected leaves (7 dpi) using DCFH-DA. (<b>D</b>) Quantification of the infected sites with callose deposition. In (<b>B</b>–<b>D</b>), the data represent means ± SE (n = 9 replicates from 3 independent experiments). Significant differences are indicated by different letters according to one-way analysis of variance and Turkey’s multiple-comparison test (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>EqSec5 and EqSec6 function together. (<b>A</b>) Y2H assay analyzing the interaction between EqSec5 and EqSec6. EqSec5 proteins were fused with the GAL4 activation domain (AD) and EqSec6 proteins with the binding domain (BD). The empty pGADT7 and pGBKT7 vectors were used as negative controls. Yeast cells containing the indicated plasmids were plated on growth medium (–Leu/–Trp) or selection medium (–Leu–Trp–His–Ade) supplemented with X-α-gal. (<b>B</b>) RT-PCR analysis for <span class="html-italic">EqSec5/6</span> overexpression. RNA samples were extracted from strains with 7 days of growth. <span class="html-italic">EqEF-1a</span> was used as the reference control. (<b>C</b>) Overexpressing <span class="html-italic">EqSec5</span> and <span class="html-italic">EqSec6</span> partially restored infection caused by the <span class="html-italic">EqSec6</span>- and <span class="html-italic">EqSec5</span>-silenced strains, respectively. The representative images were captured at 7 dpi. (<b>D</b>) Quantification of lesion area in <span class="html-italic">H. brasiliensis</span> leaves inoculated with <span class="html-italic">E. quercicola</span> strains at 7 dpi. The data represent means ± SE (n = 24 inoculated sites from 3 independent experiments). Student’s <span class="html-italic">t</span>-test was used to analyze significant differences between the two data groups (** <span class="html-italic">p</span> &lt; 0.01). In (<b>B</b>–<b>D</b>), OE-<span class="html-italic">EqSec5/6</span> is the strain overexpressing <span class="html-italic">EqSec5/6</span>.</p>
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