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16 pages, 4976 KiB  
Brief Report
Assessment of the Effect of Deleting the African Swine Fever Virus Gene R298L on Virus Replication and Virulence of the Georgia2010 Isolate
by Elizabeth Ramirez-Medina, Lauro Velazquez-Salinas, Alyssa Valladares, Ediane Silva, Leeanna Burton, Douglas P. Gladue and Manuel V. Borca
Viruses 2024, 16(12), 1911; https://doi.org/10.3390/v16121911 (registering DOI) - 13 Dec 2024
Abstract
African swine fever (ASF) is a lethal disease of domestic pigs that is currently challenging swine production in large areas of Eurasia. The causative agent, ASF virus (ASFV), is a large, double-stranded and structurally complex virus. The ASFV genome encodes for more than [...] Read more.
African swine fever (ASF) is a lethal disease of domestic pigs that is currently challenging swine production in large areas of Eurasia. The causative agent, ASF virus (ASFV), is a large, double-stranded and structurally complex virus. The ASFV genome encodes for more than 160 proteins; however, the functions of most of these proteins are still in the process of being characterized. The ASF gene R298L, which has previously been characterized as able to encode a functional serine protein kinase, is expressed late in the virus infection cycle and may be part of the virus particle. There is no description of the importance of the R298L gene in basic virus functions such as replication or virulence in the natural host. Based on its evolution, it is proposed that there are four different phenotypes of R298L of ASFV in nature, which may have potential implications for R298L functionality. We report here that a recombinant virus lacking the R298L gene in the Georgia 2010 isolate, ASFV-G-∆R298L, does not exhibit significant changes in its replication in primary cultures of swine macrophages. In addition, when experimentally inoculated in pigs, ASFV-G-∆R298L induced a fatal form of the disease similar to that caused by the parental virulent ASFV-G. Therefore, deletion of R298L does not significantly affect virus replication and virulence in domestic pigs of the ASFV Georgia 2010 isolate. Full article
(This article belongs to the Collection African Swine Fever Virus (ASFV))
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Figure 1
<p>Phylogenetic dynamics of <span class="html-italic">R298L</span> gene in nature. (<b>A</b>) Phylogenetic analysis conducted by the neighbor-joining method using the full-length sequence of the <span class="html-italic">R298L</span> gene indicates the existence of three potential phylogenetic groups. Numbers in the parenthesis indicate the genotype of different strains based on the <span class="html-italic">B646L</span> classification. Percentage of nucleotide (nt) and amino acid (AA) identities within groups are displayed. Pairwise distance analysis showing differences at the nucleotide (<b>B</b>) and amino acid level (<b>C</b>) between phylogenetic groups are exhibited. (<b>D</b>) Phylogenic tree reconstructed by the maximum likelihood method using full length amino acid sequences of <span class="html-italic">R298L</span>. ASFV labeled with the same shape indicates 100% amino acid sequence identity.</p>
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<p>Amino acid similarities of <span class="html-italic">R298L</span> among ASFV representative strains. Amino acid alignment showing similarities of the <span class="html-italic">R298L</span> protein among a group of representative ASFV isolates. The protein kinase/catalytic domain spans between residues 46 and 277. Asterisks above specific sites indicate ATP binding residues in <span class="html-italic">R298L</span>. Conservation plot scores reflect the nature of the change in specific sites. Increased scores reflect substitutions between residues with similar biological properties. The analysis was conducted in the software Jalview version 2.11.1.7.</p>
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<p>Evolutionary dynamics of <span class="html-italic">R298L</span> gene in nature. (<b>A</b>) Comparison between synonymous (dS) and nonsynonymous (dN) substitutions rates during the evolution of the <span class="html-italic">R298L</span> gene. Significant differences between dS and dN were determined by the unpaired <span class="html-italic">t</span>-test. (<b>B</b>) Graphic representation obtained by SLAC analysis, showing the ratio dN-dS at specific codon sites in the <span class="html-italic">R298L</span> gene of ASFV. Identification of specific codon sites under positive selection (blue asterisks) and negative selection (green asterisks). Orange asterisks represent sites of negative selection at internal nodes. Results were obtained by MEME (<span class="html-italic">p</span>-value threshold of 0.1) and FEL (<span class="html-italic">p</span>-value threshold of 0.1). Numbers close to the asterisk indicate the specific codon position.</p>
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<p>Ancestral reconstruction analysis of codon sites under positive selection at <span class="html-italic">R298L</span>. The analysis shows the evolutionary dynamics among the predicted phylogenetic groups in <span class="html-italic">R298L</span> at codon 36 (<b>A</b>), 137 (<b>B</b>) and 182 (<b>C</b>). Each phylogenetic tree displays the predicted codon sequences at internal nodes (most probable common ancestor sequence associated with the divergence between and within phylogenetic groups) and leaf nodes (represented by different isolates). Analysis was conducted using the algorithm MEME. Results were saved in json format and visualized in the MEME analysis result visualization tool (<a href="https://observablehq.com/@spond/meme" target="_blank">https://observablehq.com/@spond/meme</a>, accessed on 25 October 2024).</p>
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<p>Detection of epistasis/co-evolution in <span class="html-italic">R298L</span>. (<b>A</b>) Pairs of co-evolving sites identified by BMG analysis (posterior probability cutoff 0.5). P [Site 1 –&gt; Site 2] indicates the probability of site 2 to be conditionally dependent on site 1. P [Site 2 –&gt; Site 1] indicates the probability of site 1 to be conditionally dependent on site 2. P [Site 1 &lt;–&gt; Site 2] indicates the probability that sites 1 and 2 are not conditionally independent. Ancestral reconstruction analysis using MEME was conducted to show the evolutionary dynamics of pairs of co-evolving sites 114–137 (<b>B</b>) and 238–256 (<b>C</b>). For each phylogenetic tree, the predicted amino acid sequences at internal nodes are shown (most probable common ancestor sequence associated with the divergence between and within phylogenetic groups) and leaf nodes (represented by different isolates). Phylogenetic trees were obtained using the MEME analysis result visualization tool (<a href="https://observablehq.com/@spond/meme" target="_blank">https://observablehq.com/@spond/meme</a>, accessed on 25 October 2024).</p>
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<p>Expression of ASFV gene <span class="html-italic">R298L</span> in swine macrophages infected with ASFV-G. Reverse transcription qPCR was performed to assess the expression of the <span class="html-italic">R298L</span> gene at different time points post infection. Results obtained using specific qPCRs to detect the expression of ASFV genes encoding early protein p30 and late protein p72 are used as references. Transcription levels of different ASFV genes are expressed as relative quantities of mRNA accumulation (estimated by 2<sup>−ΔΔCt</sup>). Values are expressed in log<sub>10</sub>. Dashed line reflects the background (≤1.26 log<sub>10</sub> 2<sup>−ΔΔCt</sup>) associated with potential traces of DNA contamination after treatment of the RNA samples with DNase I.</p>
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<p>Schematic for the development of ASFV-G-∆R298L. The recombinant vector, containing the mCherry reporter gene under the ASFV p72 promoter activity and the gene positions are shown. The nucleotide positions of the area that was deleted in the ASFV-G genome are indicated by the dashed lines. The resulting ASFV-G-∆R298L virus with the cassette inserted is shown on the bottom.</p>
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<p>In vitro growth kinetics in primary swine macrophage cell cultures for ASFV-G-∆R298L and parental ASFV-G (MOI = 0.01). Data represent means and standard deviations of two replicas. Sensitivity using this methodology for detecting virus is ≥log10<sup>1.8</sup> HAD<sub>50</sub>/mL.</p>
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<p>Evolution of body temperature in animals (5 animals/group) IM infected with 10<sup>2</sup> HAD<sub>50</sub> of either ASFV-G-∆R298L or parental ASFV-G.</p>
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<p>Evolution of mortality in animals IM infected with 10<sup>2</sup> HAD<sub>50</sub> of either ASFV-G-∆R298L or parental virulent ASFV-G.</p>
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<p>Viremia titers detected in pigs IM inoculated with 10<sup>2</sup> HAD<sub>50</sub> of either ASFV-G-∆R298L or ASFV-G. Each symbol represents individual viremia titers in each animal in the groups. Sensitivity of virus detection: ≥log10 <sup>1.8</sup> TCID<sub>50</sub>/mL.</p>
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13 pages, 842 KiB  
Article
Genome Insights into Beneficial Microbial Strains Composing SIMBA Microbial Consortia Applied as Biofertilizers for Maize, Wheat and Tomato
by Lisa Cangioli, Silvia Tabacchioni, Andrea Visca, Alessia Fiore, Giuseppe Aprea, Patrizia Ambrosino, Enrico Ercole, Soren Sørensen, Alessio Mengoni and Annamaria Bevivino
Microorganisms 2024, 12(12), 2562; https://doi.org/10.3390/microorganisms12122562 - 12 Dec 2024
Abstract
For the safe use of microbiome-based solutions in agriculture, the genome sequencing of strains composing the inoculum is mandatory to avoid the spread of virulence and multidrug resistance genes carried by them through horizontal gene transfer to other bacteria in the environment. Moreover, [...] Read more.
For the safe use of microbiome-based solutions in agriculture, the genome sequencing of strains composing the inoculum is mandatory to avoid the spread of virulence and multidrug resistance genes carried by them through horizontal gene transfer to other bacteria in the environment. Moreover, the annotated genomes can enable the design of specific primers to trace the inoculum into the soil and provide insights into the molecular and genetic mechanisms of plant growth promotion and biocontrol activity. In the present work, the genome sequences of some members of beneficial microbial consortia that have previously been tested in greenhouse and field trials as promising biofertilizers for maize, tomato and wheat crops have been determined. Strains belong to well-known plant-growth-promoting bacterial genera such as Bacillus, Burkholderia, Pseudomonas and Rahnella. The genome size of strains ranged from 4.5 to 7.5 Mbp, carrying many genes spanning from 4402 to 6697, and a GC content of 0.04% to 3.3%. The annotation of the genomes revealed the presence of genes that are implicated in functions related to antagonism, pathogenesis and other secondary metabolites possibly involved in plant growth promotion and gene clusters for protection against oxidative damage, confirming the plant-growth-promoting (PGP) activity of selected strains. All the target genomes were found to possess at least 3000 different PGP traits, belonging to the categories of nitrogen acquisition, colonization for plant-derived substrate usage, quorum sensing response for biofilm formation and, to a lesser extent, bacterial fitness and root colonization. No genes putatively involved in pathogenesis were identified. Overall, our study suggests the safe application of selected strains as “plant probiotics” for sustainable agriculture. Full article
(This article belongs to the Special Issue Advances in Bacterial Genetics)
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<p>The difference in frequency occurrence of PGP traits (from PLaBAse) between the genomes presented in this work and the reference genome for each of them. A positive value represents an increase in the frequency occurrence of that PGP trait in the target genome, compared to the reference. A negative value represents a decrease in the frequency of occurrence of that PGP trait in the target genome, compared to the reference.</p>
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14 pages, 3375 KiB  
Article
Diversity and Characteristics of the Oral Microbiome Associated with Self-Reported Ancestral/Ethnic Groups
by Qingguo Wang, Bing-Yan Wang, She’Neka Williams and Hua Xie
Int. J. Mol. Sci. 2024, 25(24), 13303; https://doi.org/10.3390/ijms252413303 - 11 Dec 2024
Viewed by 280
Abstract
Periodontitis disproportionately affects genetic ancestral/ethnic groups. To characterize the oral microbiome from different genetic ancestral/ethnic groups, we collected 161 dental plaque samples from self-identified African Americans (AAs), Caucasian Americans (CAs), and Hispanic Americans (HAs) with clinical gingival health or biofilm-induced gingivitis on an [...] Read more.
Periodontitis disproportionately affects genetic ancestral/ethnic groups. To characterize the oral microbiome from different genetic ancestral/ethnic groups, we collected 161 dental plaque samples from self-identified African Americans (AAs), Caucasian Americans (CAs), and Hispanic Americans (HAs) with clinical gingival health or biofilm-induced gingivitis on an intact periodontium. DNA was extracted from these samples, and then DNA libraries were prepared and sequenced using an Illumina NovaSeq high-throughput sequencer. We found significant differences in the diversity and abundance of microbial taxa among dental plaque samples of the AA, CA, and HA groups. We also identified unique microbial species in a self-reported ancestral/ethnic group. Moreover, we revealed variations in functional potentials of the oral microbiome among the three ancestral/ethnic groups, with greater diversity and abundance of antibiotic-resistant genes in the oral microbiome and significantly more genes involved in the modification of glycoconjugates and oligo- and polysaccharides in AAs than in CAs and HAs. Our observations suggest that the variations in the oral microbiome associated with ancestral/ethnic backgrounds may directly relate to their virulence potential including their abilities to induce host immune responses and to resist antibiotic treatment. These finding can be a steppingstone for developing precision medicine and personalized periodontal prevention/treatment and for reducing oral health disparities. Full article
(This article belongs to the Section Molecular Biology)
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<p>Comparison of the number of non-redundant genes in dental plaque samples from different ancestral/ethnic groups. (<b>A</b>) The violins represent the richness of non-redundant genes in the samples with low levels of <span class="html-italic">P. gingivalis</span> in AAs (Gpg1A), CAs (Gpg1C), and HAs (Gpg1H) or high levels of <span class="html-italic">P. gingivalis</span> in AAs (Gpg2A), CAs (Gpg2C), and HAs (Gpg2H). (<b>B</b>) Venn diagram of the total number of non-redundant genes identified in the dental plaque samples from AAs (GpgA), CAs (GpgC), and (GpgH).</p>
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<p>Venn diagram of the microbial taxa at the species level identified in the genetic ancestral/ethnic groups for AAs (GpgA), CAs (GpgC), and HAs (GpgH).</p>
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<p>Visualization of NMDS analysis. Dots in a two-dimensional space represent dental plaque samples, and the distance between each pair of dots represents the dissimilarity between the corresponding two samples. The samples in the same groups were assigned the same colors.</p>
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<p>Diversity and abundance of antibiotic-resistant genes (ARGs). (<b>A</b>) Total number of ARG classes, and (<b>B</b>) the abundance of ARGs in each sample. Each sample is presented as a red point, and the number in each boxplot represents median number of ARG classes and abundance in a group.</p>
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<p>Antibiotic-resistant gene mechanism and the loop graph of species distribution. Circle chart is divided into two parts: the right side shows the sample information, and the left side shows the ARG tolerance of antibiotic information. Different colors in the inner circle represent different samples and ARGs and the scale for the relative abundance (unit ppm). The left side is the sum of the relative abundance of the resistance genes in the sample, and the right side is the sum of the relative abundance of the resistance genes in each ARG. The left side of the outer circle shows the relative percentage of the antibiotic to which the resistance gene belongs, and the right side of the outer ring shows the relative percentage of the sample in which the antibiotic-resistant gene is located.</p>
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<p>Heatmap of carbohydrate-active enzyme genes identified in AA, CA, and HA groups. Columns represent sample groups AA, CA, and HA, and rows represent genes. The red color represents high gene abundance to contrast with low abundance in blue color. The rows and columns are ordered based on the correlations of z scores, which were calculated based on gene abundance.</p>
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13 pages, 448 KiB  
Review
A Decade-Long Review of the Virulence, Resistance, and Epidemiological Risks of Klebsiella pneumoniae in ICUs
by Tao-An Chen, Ya-Ting Chuang and Chieh-Hui Lin
Microorganisms 2024, 12(12), 2548; https://doi.org/10.3390/microorganisms12122548 - 11 Dec 2024
Viewed by 303
Abstract
Klebsiella pneumoniae, a major opportunistic pathogen, causes severe infections in both community and healthcare settings, especially in intensive care units (ICUs), where multidrug-resistant (MDR) strains, such as carbapenem-resistant K. pneumoniae (CRKP), pose significant treatment challenges. The rise in hypervirulent K. pneumoniae (hvKP) [...] Read more.
Klebsiella pneumoniae, a major opportunistic pathogen, causes severe infections in both community and healthcare settings, especially in intensive care units (ICUs), where multidrug-resistant (MDR) strains, such as carbapenem-resistant K. pneumoniae (CRKP), pose significant treatment challenges. The rise in hypervirulent K. pneumoniae (hvKP) with enhanced virulence factors complicates management further. The ST11 clone, prevalent in China, exhibits both resistance and virulence traits, contributing to hospital outbreaks. ICU patients, particularly those with comorbidities or prior antibiotic exposure, are at higher risk. Treatment is complicated by limited antibiotic options and the increasing prevalence of polymicrobial infections, which involve resistant pathogens like Pseudomonas aeruginosa and Acinetobacter baumannii. Combination therapies offer some promise, but mortality rates remain high, and resistance to last-resort antibiotics is growing. Infection control measures and personalized treatment plans are critical, alongside the urgent need for vaccine development to combat the rising threat of K. pneumoniae, particularly in vulnerable populations. Effective management requires improved diagnostic tools, antimicrobial stewardship, and innovative treatment strategies to reduce the burden of this pathogen, especially in resource-limited settings. This review aims to provide a comprehensive analysis of the virulence, resistance, and epidemiological risks of K. pneumoniae in ICUs over the past decade, highlighting the ongoing challenges and the need for continued efforts to combat this growing threat. Full article
(This article belongs to the Special Issue Virulence and Resistance of Klebsiella pneumoniae, 2nd Edition)
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<p>Challenges of CRKP infections in the ICU.</p>
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13 pages, 2510 KiB  
Article
Characterization of the CBM50 Gene Family in Tilletia horrida and Identification of the Putative Effector Gene ThCBM50_1
by Ting Xiang, Deze Xu, Linxiu Pan, Dongyu Zhai, Yu Zhang, Aiping Zheng, Desuo Yin and Aijun Wang
J. Fungi 2024, 10(12), 856; https://doi.org/10.3390/jof10120856 - 11 Dec 2024
Viewed by 223
Abstract
Carbohydrate-binding modules (CBMs) are essential virulence factors in phytopathogens, particularly the extensively studied members from the CBM50 gene family, which are known as lysin motif (LysM) effectors and which play crucial roles in plant–pathogen interactions. However, the function of CBM50 in Tilletia horrida [...] Read more.
Carbohydrate-binding modules (CBMs) are essential virulence factors in phytopathogens, particularly the extensively studied members from the CBM50 gene family, which are known as lysin motif (LysM) effectors and which play crucial roles in plant–pathogen interactions. However, the function of CBM50 in Tilletia horrida has yet to be fully studied. In this study, we identified seven CBM50 genes from the T. horrida genome through complete sequence analysis and functional annotation. Their phylogenetic relationships, conserved motifs, promoter elements, and expression profile were further analyzed. The phylogenetic analysis indicated that these seven ThCBM50 genes were divided into three groups, and close associations were observed among proteins with similar protein motifs. The promoter cis-acting elements analysis revealed that these ThCBM50 proteins may be involved in the regulation of the phytohormones, stress response, and meristem expression of the host plant during T. horrida infection. The transcriptome data indicated that four ThCBM50 genes were upregulated during T. horrida infection. We further found that ThCBM50_1 caused cell death in the leaves of Nicotiana benthamiana, and its signal peptide (SP) had a secreting function. These results offer important clues that highlight the features of T. horrida CBM50 family proteins and set the stage for further investigation into their roles in the interactions between T. horrida and rice. Full article
(This article belongs to the Special Issue Pathogenic Fungal–Plant Interactions)
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<p>The phylogenetic trees of ThCBM50 proteins and their homologous proteins in other smut fungi, included <span class="html-italic">T. laevis</span>, <span class="html-italic">T. caries</span>, <span class="html-italic">T. controversa</span>, and <span class="html-italic">T. anomala</span>. The phylogenetic tree was constructed with MEGA7 software using the neighbor-joining (NJ) method. Different groups are illustrated as branches and frames with different colors.</p>
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<p>The motif distribution, conserved domain, and gene structure analysis of the seven ThCBM50 members: (<b>A</b>) the conserved motifs of the seven ThCBM50s were analyzed using the website Multiple Em for Motif Elicitation (MEME); (<b>B</b>) the conserved motif sequence logos of ThCBM50 proteins; (<b>C</b>) the conserved functional domains of the seven ThCBM50s were analyzed using the NCBI website; (<b>D</b>) exon–intron structures.</p>
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<p><span class="html-italic">Cis</span>-acting elements of ThCBM50 family members in <span class="html-italic">Tilletia horrida</span>. The color represents various <span class="html-italic">cis</span>-regulatory elements identified in the ThCBM50 genes’ promoter regions. The circle size indicates the frequency of each <span class="html-italic">cis</span>-regulatory element.</p>
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<p>Three-dimensional (3D) modeling of ThCBM50 proteins was performed, and the results are displayed at a confidence level of &gt;0.7.</p>
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<p>Different expression patterns of five ThCBM50 genes (FDR &lt; 0.05 and |log2 fold change| &gt; 1) after the inoculation of a rice variety susceptible to <span class="html-italic">T. horrida</span> infection (9311A). The heatmap was constructed based on the expression level of each FPKM value of the ThCBM50 genes from RNA-Seq data. Colors ranging from green to pink in the boxes indicate expression levels from lowest to highest. The digits on the scale indicate the expression levels after transcriptome data normalization.</p>
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<p>The expression of ThCBM50_1 after inoculation of a susceptible rice variety (9311A) with <span class="html-italic">T. horrida</span> was analyzed using qRT-PCR. UBQ expression served as an internal reference for the normalization of expression levels within the samples. Three independent experiments were performed, with four biological replicates each. The error bars indicate the standard deviation (SD) of the three independent experiments (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Functional analysis of ThCBM50_1′s signal peptide (SP) sequence (ThCBM50_1<sup>SP</sup>). (<b>A</b>) The yeast invertase secretion assay was used to identify the secretory function of ThCBM50_1<sup>SP</sup>. To cultivate the transformed YTK12 yeast strains, the CMD-W and YPRAA media with raffinose as the sole carbon source were each used. (<b>B</b>) The enzymatic activity of invertase was identified by performing the 2, 3, 5-triphenyltetrazolium chloride experiment. The SP sequence of <span class="html-italic">Phytophthora sojae</span> Avr1b was used as the positive control, and the SP sequence of <span class="html-italic">Magnaporthe oryzae</span> Mg87 was the negative control.</p>
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<p>ThCBM50_1 activated the necrosis phenotype in the epidermis cells of tobacco leaves. The green fluorescent protein (GFP) served as the negative control. BAX is a mouse protein known to induce cell death in <span class="html-italic">N. benthamiana</span> and served as the positive control. Representative photos were taken at 4 days post-infiltration (dpi).</p>
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17 pages, 4269 KiB  
Article
Potential Involvement of Buchnera aphidicola (Enterobacteriales, Enterobacteriaceae) in Biotype Differentiation of Sitobion avenae (Hemiptera: Aphididae)
by Yanyan Lan, Jingpeng Li, Shuo Zhang, Qiuju Qin, Deguang Liu, Chen Luo, Shipeng Han, Da Wang and Yunzhuan He
Insects 2024, 15(12), 980; https://doi.org/10.3390/insects15120980 - 11 Dec 2024
Viewed by 246
Abstract
Buchnera aphidicola, an obligate endosymbiont of most aphid species, can influence aphids’ host adaptability through amino acid metabolism, potentially mediating biotype differentiation. However, its role in the biotype differentiation of Sitobion avenae remains unclear. To address this issue, six S. avenae biotypes [...] Read more.
Buchnera aphidicola, an obligate endosymbiont of most aphid species, can influence aphids’ host adaptability through amino acid metabolism, potentially mediating biotype differentiation. However, its role in the biotype differentiation of Sitobion avenae remains unclear. To address this issue, six S. avenae biotypes were tested in this study. Buchnera abundance varied among biotypes fed on different wheat/barley varieties (i.e., Zhong 4 wumang, 186-TM12-34; Dulihuang, Zaoshu No.3, Xiyin No.2). The reduction in Buchnera abundance through antibiotic (rifampicin) treatment altered the virulence of five S. avenae biotypes. Based on transcriptome analysis, the differential expression of three genes (i.e., LeuB, TrpE, and IlvD) related to leucine, tryptophan, isoleucine, and valine metabolism was detected between different biotypes. Principal component analysis showed that leucine and tryptophan deficiencies most significantly impacted nymph development duration and aphid fecundity. Additionally, a neighbor-joining phylogenetic tree indicated the genetic differentiation of Buchnera among different biotypes. These results suggest Buchnera-mediated amino acid metabolism is correlated with biotype differentiation in S. avenae, although the precise mechanisms by which Buchnera influences this differentiation require further investigation. This study can offer a theoretical basis for the development of resistant crops, leading to the sustainable control of this aphid and reduced reliance on chemical insecticides. Full article
(This article belongs to the Special Issue Biology and Molecular Mechanisms of Plant-Aphid Interactions)
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Graphical abstract

Graphical abstract
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<p>The <span class="html-italic">Buchnera</span> abundance in six <span class="html-italic">Sitobion avenae</span> biotypes ((<b>A</b>), 5-day-old nymphs; (<b>B</b>), adults) on different host plants. Error bars indicate ± SE; Different uppercase letters above the bars indicate significant differences in <span class="html-italic">Buchnera</span> abundance for the same biotype on different host plants, and lowercase letters indicate significant differences in <span class="html-italic">Buchnera</span> abundance among six biotypes on the same plant (α = 0.05, ANOVA followed by Tukey’s tests).</p>
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<p>Comparisons of 10 d fecundity for six <span class="html-italic">Sitobion avenae</span> biotypes on different varieties ((<b>A</b>), before the rifampicin treatment; (<b>B</b>), after the rifampicin treatment). Error bars indicate ± SE; Different uppercase letters above the bars indicate significant differences in the fecundity for the same biotype on different host plants, and lowercase letters indicate significant differences in fecundity among six biotypes on the same plant (α = 0.05, ANOVA followed by Tukey’s tests).</p>
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<p>A heatmap of DEGs of <span class="html-italic">Buchnera</span> between two <span class="html-italic">Sitobion avenae</span> biotypes (genes with expression higher and lower than the mean are indicated by red and blue, respectively).</p>
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<p>Comparisons of 10 d fecundity for six <span class="html-italic">Sitobion avenae</span> biotypes on different artificial diets. Error bars indicate ± SE; Different uppercase letters above the bars indicate significant differences in the fecundity for the same biotype on different artificial diets, and lowercase letters indicate significant differences in fecundity among six biotypes on the same artificial diet (α = 0.05, ANOVA followed by Tukey’s tests).</p>
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<p>A neighbor-joining dendrogram based on the combined <span class="html-italic">16S rDNA</span>, <span class="html-italic">Gnd</span>, <span class="html-italic">AtpD</span>, <span class="html-italic">LeuB</span>, <span class="html-italic">IlvD</span>, <span class="html-italic">TrpE</span> datasets of <span class="html-italic">Buchnera</span> from six <span class="html-italic">Sitobion avenae</span> biotypes.</p>
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10 pages, 259 KiB  
Article
Prevalence of Vibrio spp. in Seafood from German Supermarkets and Fish Markets
by Christopher Zeidler, Vanessa Szott, Thomas Alter, Stephan Huehn-Lindenbein and Susanne Fleischmann
Foods 2024, 13(24), 3987; https://doi.org/10.3390/foods13243987 - 10 Dec 2024
Viewed by 344
Abstract
This study investigates the prevalence of Vibrio spp. in seafood from supermarkets and fish markets in Berlin, Germany. A total of 306 seafood samples, including shrimp and mussels, were bought from supermarkets between March 2023 and January 2024. Samples were analysed using the [...] Read more.
This study investigates the prevalence of Vibrio spp. in seafood from supermarkets and fish markets in Berlin, Germany. A total of 306 seafood samples, including shrimp and mussels, were bought from supermarkets between March 2023 and January 2024. Samples were analysed using the ISO standard method and multiplex PCR to identify V. parahaemolyticus, V. alginolyticus, V. cholerae and V. vulnificus. The results indicated an overall Vibrio spp. prevalence of 56%. Among the positive samples, the most prevalent species found was V. parahaemolyticus (58%), followed by V. alginolyticus (42%), V. cholerae non-O1/non-O139 (25%), and V. vulnificus (4%). Samples obtained from supermarkets exhibited a lower prevalence (50%) than those received from fish markets (91%). Virulence genes such as ctxA, tdh, or trh were not detected in the respective Vibrio species. Nevertheless, the high prevalence underscores the need and urgency of continuous seafood surveillance. Full article
(This article belongs to the Section Food Microbiology)
27 pages, 385 KiB  
Review
Current Status of Porcine Reproductive and Respiratory Syndrome Vaccines
by Honglei Wang and Wenhai Feng
Vaccines 2024, 12(12), 1387; https://doi.org/10.3390/vaccines12121387 - 10 Dec 2024
Viewed by 515
Abstract
Porcine reproductive and respiratory syndrome (PRRS), characterized by reproductive failures in breeding pigs and respiratory diseases in growing pigs, is a widespread and challenging disease. The agent, PRRSV, is a single-strand RNA virus that is undergoing continuous mutation and evolution, resulting in the [...] Read more.
Porcine reproductive and respiratory syndrome (PRRS), characterized by reproductive failures in breeding pigs and respiratory diseases in growing pigs, is a widespread and challenging disease. The agent, PRRSV, is a single-strand RNA virus that is undergoing continuous mutation and evolution, resulting in the global spread of multiple strains with different genetic characteristics and variable antigens. There are currently no effective measures to eradicate PRRS, and vaccination is crucial for controlling the disease. At present, various types of vaccine are available or being studied, including inactivated vaccines, modified live virus (MLV) vaccines, vector vaccines, subunit vaccines, DNA vaccines, RNA vaccines, etc. MLV vaccines have been widely used to control PRRSV infection for more than 30 years since they were first introduced in North America in 1994, and have shown a certain efficacy. However, there are safety and efficacy issues such as virulence reversion, recombination with field strains, and a lack of protection against heterologous strains, while other types of vaccine have their own advantages and disadvantages, making the eradication of PRRS a challenge. This article reviews the latest progress of these vaccines in the prevention and control of PRRS and provides scientific inspiration for developing new strategies for the next generation of PRRS vaccines. Full article
(This article belongs to the Special Issue Vaccines and Animal Health)
13 pages, 5038 KiB  
Article
Identification and Genome Sequencing of Novel Virulent Strains of Xanthomonas oryzae pv. oryzae Causing Rice Bacterial Blight in Zhejiang, China
by Weifang Liang, Yuhang Zhou, Zhongtian Xu, Yiyuan Li, Xinyu Chen, Chulang Yu, Fan Hou, Binfeng Dai, Liequan Zhong, Ji-An Bi, Liujie Xie, Chengqi Yan, Jianping Chen and Yong Yang
Pathogens 2024, 13(12), 1083; https://doi.org/10.3390/pathogens13121083 - 9 Dec 2024
Viewed by 373
Abstract
Xanthomonas oryzae pv. oryzae (Xoo) is the causative agent of rice bacterial blight (RBB), resulting in substantial harvest losses and posing a challenge to maintaining a stable global supply. In this study, Xoo strains isolated from Shaoxing, Quzhou, and Taizhou, where [...] Read more.
Xanthomonas oryzae pv. oryzae (Xoo) is the causative agent of rice bacterial blight (RBB), resulting in substantial harvest losses and posing a challenge to maintaining a stable global supply. In this study, Xoo strains isolated from Shaoxing, Quzhou, and Taizhou, where RBB occurred most frequently in Zhejiang Province in 2019, were selected as the subjects of research. Three isolated pathogenic bacteria of ZXooS (from Shaoxing), ZXooQ (from Quzhou), and ZXooT (from Taizhou) were all identified as novel Xoo strains. These novel strains demonstrate greater virulence compared to Zhe173, the previous epidemic Xoo strain from Zhejiang Province. Subsequent genomic sequencing and analysis revealed that there existed significant differences in the genome sequence, especially in effector genes corresponding to some known rice resistance (R) genes between the novel strains and Zhe173. The sequence alignment of avirulent genes (effector genes) indicated that nucleic and amino acid sequences of AvrXa5, AvrXa7, AvrXa10, and AvrXa23 in the novel strains varied prominently from those in Zhe173. Interestingly, it seemed that only the genome of ZXooQ might contain the AvrXa3 gene. In addition, the phylogenetic analysis of 61 Xoo strains revealed that the novel strains were situated in a distinct evolutionary clade separate from Zhe173. These results here suggest that the emergence of novel Xoo strains may lead to resistance loss of some R genes used in commercial rice varieties, potentially serving as one of the factors leading to RBB resurgence in Zhejiang Province, China. Full article
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<p>Isolation and identification of pathogenic bacteria. (<b>A</b>–<b>C</b>): Occurrence of RBB in Shaoxing, Quzhou, and Taizhou rice regions of Zhejiang Province. (<b>D</b>): Colony morphologies of <span class="html-italic">Xoo</span> strains from left to right are as follows: Zhe173, Shaoxing (ZXooS), Quzhou (ZXooQ), and Taizhou (ZXooT). (<b>E</b>): Lesion length of Yongyou19 after inoculation with Zhe173, ZXooS, ZXooQ, and ZXooT. Statistical analysis was performed by <span class="html-italic">t</span>-test (*** <span class="html-italic">p</span> &lt; 0.001). (<b>F</b>): Disease phenotypes of Yongyou19 after inoculation with Zhe173, ZXooS, ZXooQ, and ZXooT. Bar = 1 cm.</p>
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<p>Genome features of <span class="html-italic">Xanthomonas oryzae</span> pv. <span class="html-italic">oryzae</span> Zhe173 (<b>A</b>), ZXooS (<b>B</b>), ZXooQ (<b>C</b>), and ZXooT (<b>D</b>). The outermost circle represents the genome sequence position coordinates. Moving from the outside to the inside, it includes the coding genes, gene function annotation results, ncRNA, and genomic GC content. The inner red part indicates that the GC content of this region is lower than the average GC content of the whole genome, while the outer green part indicates the opposite. The higher the peak value, the greater the difference from the average GC content and the genomic GC skew value. The inner pink part indicates that the content of G is lower than the content of C in this region, while the outer green part indicates the opposite.</p>
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<p>Phylogenetic tree of 61 <span class="html-italic">Xoo</span> strains from various regions. Red represents the African strains; yellow represents the South Asian strains (India, Nepal), Southeast Asian strains (Philippines), and East Asian strain (Yunnan, China); blue represents the strains from India, Thailand, and China; purple represents East Asian strains from North China and Korea; green represents South China strains; and orange represents other Chinese and Japanese strains.</p>
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<p>Comparison of whole genome and TALEs in <span class="html-italic">Xoo</span> strains. (<b>A</b>) Progressive Mauve alignment chromosomes of <span class="html-italic">Xoo</span> strains. The ruler indicates the distance from the annotated origin in base pairs. (<b>B</b>) The Tal genes of <span class="html-italic">Xoo</span> strains. Gene orientations are indicated by arrows. The identical effectors with the same RVD sequence are labeled with the same color and connected by lines. The effectors are identified based on genome sequences and are indicated in blue without lines.</p>
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<p>Comparison results of the <span class="html-italic">Avrxa5</span> gene amino acid sequence in Zhe173, ZXooS, ZXooQ, and ZXooT strains.</p>
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12 pages, 1822 KiB  
Review
H3K4 Methylation and Demethylation in Fungal Pathogens: The Epigenetic Toolbox for Survival and Adaptation in the Host
by Maruti Nandan Rai and Rikky Rai
Pathogens 2024, 13(12), 1080; https://doi.org/10.3390/pathogens13121080 - 9 Dec 2024
Viewed by 375
Abstract
Pathogenic fungi represent a diverse group of eukaryotic microorganisms that significantly impact human health and agriculture. In recent years, the role of epigenetic modifications, particularly histone modifications, in fungal pathobiology has emerged as a prominent area of interest. Among these modifications, methylation of [...] Read more.
Pathogenic fungi represent a diverse group of eukaryotic microorganisms that significantly impact human health and agriculture. In recent years, the role of epigenetic modifications, particularly histone modifications, in fungal pathobiology has emerged as a prominent area of interest. Among these modifications, methylation of histone H3 at lysine-4 (H3K4) has garnered considerable attention for its implications in regulating gene expression associated with diverse cellular processes. A body of literature has uncovered the pivotal roles of H3K4 methylation in multiple biological processes crucial for pathogenic adaptation in a wide range of fungal pathogens of humans and food crops. This review delves into the recent advancements in understanding the impact of H3K4 methylation/demethylation on fungal pathogenesis. We explore the roles of H3K4 methylation in various cellular processes, including fungal morphogenesis and development, genome stability and DNA repair, metabolic adaptation, cell wall maintenance, biofilm formation, antifungal drug resistance, and virulence. We also discuss the conservation of H3K4 methylation regulators and their potential as therapeutic targets to prevent fungal diseases. Collectively, this review underscores the intricate links between H3K4 methylation, fungal pathogenesis, and potential avenues for novel antifungal strategies. Full article
(This article belongs to the Section Fungal Pathogens)
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<p>Graphical abstract of histone methylation-mediated transcriptional activation. Created in BioRender. Rai, N. (2024) <a href="https://BioRender.com/m31m407" target="_blank">https://BioRender.com/m31m407</a>, 2 December 2024.</p>
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<p>A graphical representation of the multifaceted roles of H3K4 methylation in the pathogenic adaptation of human and plant pathogenic fungi. Created in BioRender. Rai, N. (2024) <a href="https://BioRender.com/q87f061" target="_blank">https://BioRender.com/q87f061</a>, 2 December 2024.</p>
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21 pages, 5660 KiB  
Article
Exploring Imaging Techniques for Detecting Tomato Spotted Wilt Virus (TSWV) Infection in Pepper (Capsicum spp.) Germplasms
by Eric Opoku Mensah, Hyeonseok Oh, Jiseon Song and Jeongho Baek
Plants 2024, 13(23), 3447; https://doi.org/10.3390/plants13233447 - 9 Dec 2024
Viewed by 337
Abstract
Due to the vulnerability of pepper (Capsicum spp.) and the virulence of tomato spotted wilt virus (TSWV), seasonal shortages and surges of prices are a challenge and thus threaten household income. Traditional bioassays for detecting TSWV, such as observation for symptoms and [...] Read more.
Due to the vulnerability of pepper (Capsicum spp.) and the virulence of tomato spotted wilt virus (TSWV), seasonal shortages and surges of prices are a challenge and thus threaten household income. Traditional bioassays for detecting TSWV, such as observation for symptoms and reverse transcription-PCR, are time-consuming, labor-intensive, and sometimes lack precision, highlighting the need for a faster and more reliable approach to plant disease assessment. Here, two imaging techniques—Red–Green–Blue (RGB) and hyperspectral imaging (using NDVI and wavelength intensities)—were compared with a bioassay method to study the incidence and severity of TSWV in different pepper accessions. The bioassay results gave TSWV an incidence from 0 to 100% among the accessions, while severity ranged from 0 to 5.68% based on RGB analysis. The normalized difference vegetative index (NDVI) scored from 0.21 to 0.23 for healthy spots on the leaf but from 0.14 to 0.19 for disease spots, depending on the severity of the damage. The peak reflectance of the disease spots on the leaves was identified in the visible light spectrum (430–470 nm) when spectral bands were studied in the broad spectrum (400.93–1004.5 nm). For the selected wavelength in the visible light spectrum, a high reflectance intensity of 340 to 430 was identified for disease areas, but between 270 and 290 for healthy leaves. RGB and hyperspectral imaging techniques can be recommended for precise and accurate detection and quantification of TSWV infection. Full article
(This article belongs to the Special Issue Plant Diseases and Sustainable Agriculture)
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<p>Line graph showing the progressive growth of plant accessions based on top-view area (cm<sup>2</sup>), disease area (cm<sup>2</sup>), and disease severity (%) of the accessions, species, and the disease severity status of the pepper infected with TSWV-YI. For the graph on accessions, the red lines represent accessions with high disease severity, the black lines represent accessions with moderate disease severity and the green lines represent accessions with low disease severity to TSWV-YI.</p>
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<p>RGB images showing infection trends among <span class="html-italic">low</span>, <span class="html-italic">moderate</span>, and <span class="html-italic">high</span> disease severity to TSWV-YI infection of three selected accessions (IT218962—low severity, IT136625—moderate severity, and IT158568—high severity). Infected—plants inoculated with the TSWV; non-infected—the non-inoculated plants (control). The values in white are the average total areas (in cm<sup>2</sup>) of the five plants per accession, measured by the top-view camera at a distance of 0.6 m from the plants, while the values in black are the average infected parts (in cm<sup>2</sup>) of the five plants per accession, based on the image segmentation.</p>
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<p>Hyperspectral analysis—hyperspectral images showing normalized difference vegetation index (NDVI) of selected accessions (IT218962—low severity, IT136625—moderate severity, IT158568—high severity, and IT284034—non-infected) based on TSWV symptoms assessments.</p>
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<p>Hyperspectral reflectance of five spots on the leaf surfaces of high, moderate, and low severities, and non-infected plants. (<b>A</b>,<b>B</b>,<b>E</b>,<b>F</b>) from 400.93 to 1004.5 nm and (<b>C</b>,<b>D</b>,<b>G</b>,<b>H</b>) from 430 to 470 nm wavelengths on the electromagnetic spectrum. The bands indicate the number of spots selected on the plants. The broken black arrows show the band spot at which the infected areas were separated from the normal areas, and where symptoms of TSWV were assumed to be detected.</p>
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<p>RGB and hyperspectral imaging for TSWV-YI common symptoms in pepper germplasm—mosaic, necrosis, ring spot, puckered leaf, and non-infected plant. NDVI—normalized difference vegetation index.</p>
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<p>RGB and hyperspectral imaging boxes used for the experiment. (<b>A</b>) The RGB imaging set up with a Sony<sup>®</sup> camera, plants, scale bar, and flashlights. (<b>B</b>) Imaging box containing an infra-red camera, RGB camera, hyperspectral camera, and flashlights. A conveyor moves the plant for imaging. A scale bar was included for image segmentation and unit conversion from pixels into mm.</p>
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<p>RGB image processing, segmentation, and measurement. (<b>A</b>) Raw image from the top view Sony<sup>®</sup> camera. (<b>B</b>) Scale bar to convert units from pixels into mm<sup>2</sup>. (<b>C</b>) Image background noise removed. (<b>D</b>) Total leaf area masked in red. (<b>E</b>) Disease leaf area segmented as a red mask. (<b>F</b>) Disease area selected for measurement.</p>
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<p>Hyperspectral image analysis process. (<b>A</b>,<b>B</b>)—band detection, (<b>C</b>,<b>D</b>)—intensity of TSWV infected parts of the plant, and (<b>E</b>)—NDVI plot showing areas affected by the virus based on a scale (green indicating low intensity and red indicating high intensity). The black arrow shows the band spot where the TSWV infection was assumed to be detected.</p>
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13 pages, 4067 KiB  
Article
Infectious Bursal Disease Virus in Algeria: Persistent Circulation of Very Virulent Strains in Spite of Control Efforts
by Chafik Redha Messaï, Nadia Safia Chenouf, Oussama Khalouta, Abdelhafid Chorfa, Omar Salhi, Claudia Maria Tucciarone, Francesca Poletto, Giovanni Franzo, Chahrazed Aberkane, Mattia Cecchinato and Matteo Legnardi
Animals 2024, 14(23), 3543; https://doi.org/10.3390/ani14233543 - 8 Dec 2024
Viewed by 516
Abstract
Infectious bursal disease (IBD) is among the most impactful immunosuppressive diseases of poultry. Its agent, infectious bursal disease virus (IBDV), is prone to both mutation and reassortment, resulting in a remarkable variability. Traditionally, IBDV characterization relies on antigenicity and pathogenicity assessment, but multiple [...] Read more.
Infectious bursal disease (IBD) is among the most impactful immunosuppressive diseases of poultry. Its agent, infectious bursal disease virus (IBDV), is prone to both mutation and reassortment, resulting in a remarkable variability. Traditionally, IBDV characterization relies on antigenicity and pathogenicity assessment, but multiple phylogenetic classifications have been recently proposed, whose implementation in molecular surveys helps generating informative and standardized epidemiological data. In the present study, the Algerian IBDV scenario was assessed based on the novel classification guidelines by sequencing portions of both genome segments. Seventy pools of bursal samples were collected in 2022–2023 in 11 districts of Northern Algeria, mostly from broiler flocks. Out of 55 (78.6%) positive flocks, 40 (57.1%) were infected by field strains, which were characterized as very virulent strains (genotype A3B2) and phylogenetically related to previously reported Algerian strains. Significant differences in the percentage of field infections were observed between vaccinated (25/52, 46.2%) and unvaccinated (14/17, 82.3%) groups, and also between birds immunized with live intermediate (13/20, 65.0%) and intermediate plus (10/28, 35.7%) vaccines. Nonetheless, the number of field strain detections suggests a high infectious pressure and the inadequacy of current vaccination efforts, demanding a reevaluation of control measures coupled with attentive monitoring activities. Full article
(This article belongs to the Section Poultry)
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<p>Phylogenetic trees of VP2 (<b>left</b>) and VP1 (<b>right</b>) sequences, inferred with the Maximum Likelihood Method (1000 bootstraps) adopting the K2+G [<a href="#B37-animals-14-03543" class="html-bibr">37</a>] and GTR+G+I [<a href="#B38-animals-14-03543" class="html-bibr">38</a>] substitution models, respectively. Sequences are color-coded according to their genogroup classification, which refers to the criteria proposed by Islam et al. [<a href="#B18-animals-14-03543" class="html-bibr">18</a>]. Newly obtained sequences are marked with black circles (⬤), whereas other Algerian strains retrieved from GenBank are highlighted with a white square (▢). Node support values are shown only when equal to or higher than 0.7.</p>
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<p>Distribution of field A3B2 strain detections at commune level. The area shown in the main map is highlighted in light green in the inset map in the top left corner, which shows the chicken population distribution according to the Gridded Livestock of the World 4 (GLW4) dataset [<a href="#B39-animals-14-03543" class="html-bibr">39</a>].</p>
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<p>Mosaic plot showing the relationship between the administered vaccination protocol and the detection of field strains. The size of each cell is proportional to the respective count, while the colors represent standardized residuals.</p>
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26 pages, 8783 KiB  
Review
Intricate Structure–Function Relationships: The Case of the HtrA Family Proteins from Gram-Negative Bacteria
by Urszula Zarzecka and Joanna Skorko-Glonek
Int. J. Mol. Sci. 2024, 25(23), 13182; https://doi.org/10.3390/ijms252313182 - 7 Dec 2024
Viewed by 687
Abstract
Proteolytic enzymes play key roles in living organisms. Because of their potentially destructive action of degrading other proteins, their activity must be very tightly controlled. The evolutionarily conserved proteins of the HtrA family are an excellent example illustrating strategies for regulating enzymatic activity, [...] Read more.
Proteolytic enzymes play key roles in living organisms. Because of their potentially destructive action of degrading other proteins, their activity must be very tightly controlled. The evolutionarily conserved proteins of the HtrA family are an excellent example illustrating strategies for regulating enzymatic activity, enabling protease activation in response to an appropriate signal, and protecting against uncontrolled proteolysis. Because HtrA homologs play key roles in the virulence of many Gram-negative bacterial pathogens, they are subject to intense investigation as potential therapeutic targets. Model HtrA proteins from bacterium Escherichia coli are allosteric proteins with reasonably well-studied properties. Binding of appropriate ligands induces very large structural changes in these enzymes, including changes in the organization of the oligomer, which leads to the acquisition of the active conformation. Properly coordinated events occurring during the process of HtrA activation ensure proper functioning of HtrA and, consequently, ensure fitness of bacteria. The aim of this review is to present the current state of knowledge on the structure and function of the exemplary HtrA family proteins from Gram-negative bacteria, including human pathogens. Special emphasis is paid to strategies for regulating the activity of these enzymes. Full article
(This article belongs to the Special Issue Mechanism of Enzyme Catalysis: When Structure Meets Function)
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<p>The role of the HtrA homologs in Gram-negative pathogenic bacteria. HtrAs are important in the various processes that are crucial for virulence and counteract the consequences of stress conditions. (<b>a</b>) HtrAs can function as protein quality control system components by digesting improperly folded proteins and preventing their aggregation. (<b>b</b>) Transcription of the σ<sup>E</sup>-dependent genes requires digestion of the anti-sigma factor RseA. In response to the presence of unfolded OMPs, DegS becomes activated and cleaves RseA, and these events finally lead to the release of the transcriptional factor σ<sup>E</sup>. (<b>c</b>) HtrAs are involved in secretion and proper maturation of several virulence factors. (<b>d</b>) HtrAs can be secreted outside the bacterial cell (on OMVs or by another hitherto unrecognized route). In the extracellular space, HtrAs act as virulence factors by cleavage of the cell junction proteins (E-cadherin, occludin, and claudin), or/and digestion of the extracellular matrix (ECM) components.</p>
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<p>Comparison of (<b>A</b>) domain organization and (<b>B</b>) amino acid sequences of the selected HtrA homologs from Gram-negative bacteria. The amino acid sequences of HtrA <span class="html-italic">C. jejuni</span> (Q0P928), HtrA <span class="html-italic">H. pylori</span> (G2J5T2), DegP/HtrA <span class="html-italic">E. coli</span> (P0C0V0), and HtrA <span class="html-italic">C. trachomatis</span> (A0A0H2X3D0) were compared using PROMALS3D alignment [<a href="#B27-ijms-25-13182" class="html-bibr">27</a>]. The important regulatory loops (LA, LD, L1, L2, L3) and domain organization are marked. PD stands for the protease domain, T—catalytic triad, TM—transmembrane region, SP—signal peptide. Black asterisks indicate the conserved residues. The red asterisk indicates position of the 170 residue in HtrA<span class="html-italic"><sub>Hp</sub></span> (position of naturally occurring Ser/Leu substitution, affecting the stability of the trimer).</p>
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<p>Schematic illustration of the main secondary structures within the proteolytic domain. β-strands are depicted as broad arrows. α-helical structures have been omitted for clarity (modified from [<a href="#B22-ijms-25-13182" class="html-bibr">22</a>]). The regulatory loops L1, L2, L3, LA, and LD are indicated. The active side triad residues Ser, His, and Asp are shown as S, H, and D, respectively. The loop connecting N- and C-terminal β-barrels is shown as a black dashed line.</p>
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<p>Schematic representations of the <span class="html-italic">E. coli</span> (<b>A</b>) DegS and (<b>B</b>) DegP trimeric units. Scheme of the DegS trimer is based on [<a href="#B11-ijms-25-13182" class="html-bibr">11</a>], while that of the DegP trimer is based on [<a href="#B1-ijms-25-13182" class="html-bibr">1</a>].</p>
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<p>Schematic representation of the <span class="html-italic">E. coli</span> DegP higher-order oligomers. (<b>A</b>) Hexamer. (<b>B</b>) Higher-order apo-oligomers according to [<a href="#B29-ijms-25-13182" class="html-bibr">29</a>]; blue and red stars indicate position of the PDZ1 and PDZ2 domains. As can be seen, not all PDZ domains are engaged in the inter-trimer interactions in these assemblies. (<b>C</b>) Higher-order holo-oligomers according to [<a href="#B30-ijms-25-13182" class="html-bibr">30</a>]; all PDZ domains participate in the inter-trimer connections (not shown for the clarity of the schemes). (<b>D</b>) Bowl-shaped oligomers according to [<a href="#B31-ijms-25-13182" class="html-bibr">31</a>].</p>
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<p>Allosteric activation of DegS. (<b>A</b>–<b>C</b>) The resting state of the protease based on PDB entry 4RR1, and (<b>D</b>–<b>F</b>) the active state with bound ligand (shown in magenta) based on PDB entry 4RQZ. Active site residues were shown as yellow stars. The schemes (<b>E</b>,<b>F</b>) are based on [<a href="#B22-ijms-25-13182" class="html-bibr">22</a>,<a href="#B35-ijms-25-13182" class="html-bibr">35</a>]. Activation cluster is shown as a dashed line.</p>
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<p>Schematic model of allosteric activation of DegP. The activating peptide/substrate (shown in blue) binds simultaneously to the PDZ1 domain of one protomer (green) and the active center of the neighboring protomer (red). The signals are transmitted from both binding sites and they presumably meet at the LD’ loop. The activation cluster (dashed line) is formed, leading to activation of the protease. Based on [<a href="#B10-ijms-25-13182" class="html-bibr">10</a>,<a href="#B22-ijms-25-13182" class="html-bibr">22</a>,<a href="#B45-ijms-25-13182" class="html-bibr">45</a>].</p>
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<p>The domain swapping in the HtrA<span class="html-italic"><sub>Hp</sub></span> trimer. (<b>A</b>) Individual subunits a, b, and c are marked in red, blue, and green, respectively. The black arrows indicate the N-terminal region of the “b” subunit, which penetrates the “a” and “c” subunits. (<b>B</b>) Enlargement of the area where domain swapping occurs. Based on PDB entry 5Y28. The protein structure images were generated using PyMOL version 1.3.</p>
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<p>Potential interactions of Asp173 (D173) with amino acid residues located at three monomers “a”, “b”, and “c” are shown on the entire structure (<b>A</b>) and fragment in detail (<b>B</b>). Hydrogen bonds are shown as blue dashed lines. Potential hydrogen bonds are shown as a red dotted line and question mark. The residues discussed in the text are marked in colors: R31 (magenta), S43 (orange), H45 (cyan), S170 (yellow), and D173 (hot pink). Based on PDB entry 5Y28. The protein structure images were generated using PyMOL version 1.3.</p>
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<p>Location of the Ser170 residue at the subunit interface in the HtrA<span class="html-italic"><sub>Hp</sub></span>ΔPDZ2 trimer: (<b>A</b>) top view, (<b>B</b>) side view, (<b>C</b>) enlargement of the contact region of the three subunits “a” (red) “b” (blue), and “c” (green) near the residue 170 (based on PDB entry 5Y28). Positions of Arg 31 (R31), His 45 (H45), and Ser 170 (S170), each of which is located on a different monomer, were marked as (<b>A</b>) balls and (<b>B</b>) sticks colored in magenta, cyan, and yellow, accordingly. The structure images were generated using PyMOL version 1.3.</p>
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22 pages, 3175 KiB  
Article
Anthracocystis panici-leucophaei: A Potential Biological Control Agent for the Grassy Weed Digitaria insularis
by Adriany Pena de Souza, Juliana Fonseca Alves, Eliane Mayumi Inokuti, Fernando Garcia, Bruno Wesley Ferreira, Thaisa Ferreira da Nobrega, Robert Weingart Barreto, Bruno Sérgio Vieira and Camila Costa Moreira
Agronomy 2024, 14(12), 2926; https://doi.org/10.3390/agronomy14122926 - 7 Dec 2024
Viewed by 418
Abstract
Anthracocystis panici-leucophaei, causal agent of smut on Digitaria insularis (sourgrass), was evaluated as a biological control agent for this weed. Two types of inocula (teliospore and sporidia) were tested to assess its infectivity. The effects of teliospore and sporidia inoculations at different [...] Read more.
Anthracocystis panici-leucophaei, causal agent of smut on Digitaria insularis (sourgrass), was evaluated as a biological control agent for this weed. Two types of inocula (teliospore and sporidia) were tested to assess its infectivity. The effects of teliospore and sporidia inoculations at different phenological stages of sourgrass were compared, as well as the potential of sporidia and teliospores in post-emergence sourgrass management. Virulence tests were conducted with the isolates obtained from D. insularis and evaluation of specificity of A. panici-leucophaei. Both teliospores and sporidia of A. panici-leucophaei are infective to D. insularis in three different phenological stages. Newly emerged plants with one pair of leaves are more sensitive to A. panici-leucophaei. Infection by A. panici-leucophaei inhibits the growth of sourgrass, decreasing several physiological parameters of D. insularis plants. The fungus produces systematic infection of sourgrass plants and may induce the formation of sori in a significant proportion of the plant panicles, partly castrating those plants. Among sixteen A. panici-peucophaei isolates tested, isolate 46 was the most virulent and inhibited the growth of sourgrass plants, and thus appears to have good potential as a biological control agent to be deployed against sourgrass. A. panici-leucophaei was demonstrated to be specific to D. insularis. Full article
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<p>Collection sites (red triangles) [IBGE (Instituto Brasileiro de Geografia e Estatística 2021). Design ilustration: Xavier, L. C. M. (2023)].</p>
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<p>Sourgrass (<span class="html-italic">Digitaria insularis</span>) showing smut symptoms (growth reduction and formation of sori) on <span class="html-italic">Anthracocystis panici-leucophaei</span> in a field situation in Monte Carmelo, Minas Gerais (Brazil).</p>
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<p><span class="html-italic">Anthracocystis panici-leucophaei.</span> (<b>a</b>,<b>b</b>) Sori on live plants (<span class="html-italic">D. insularis</span>). (<b>c</b>,<b>d</b>) Teliospores produced on fertile hyphae forming chains while immature (SEM). (<b>e</b>) Verruculose teliospores (SEM); (<b>f</b>) Detail of teliospores production. Scale bar = 2 µm.</p>
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<p>Multilocus phylogenetic tree of <span class="html-italic">Anthracocystis</span> species inferred from RAxML and Bayesian analysis based on ITS sequences. The bootstraps ≥ 70 and Bayesian posterior probabilities ≥ 0.90 are indicated above the nodes, respectively. Isolate from the study are highlighted in bold. The tree was rooted with <span class="html-italic">Langdonia confusa</span> and <span class="html-italic">Triodiomyces triodiae</span>. The type isolate was identified as “t”, isotype as “i”, and holotype as “h” during isolates identification.</p>
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<p><span class="html-italic">D. insularis</span> plants 37 days after sowing. Control on the left followed by examples of plants inoculated with <span class="html-italic">Anthracocystis panici-leucophaei</span> on the right. Inoculated plants as follows: teliospores in the soil (TS1), sporidia in the soil (ES3), teliospores on 3–4 leaves plants (TA1), sporidia on 3–4 leaves plants (EA3), teliospores on newly emerged plants (TRE1), and sporidia on newly emerged plants (ERE3).</p>
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<p>Examples of impact of isolates of <span class="html-italic">A. panici-leucophaei</span> on sourgrass growth 90 days after sowing 80 days after inoculation with sporidial suspension on one pair of leaves-plants. (<b>A</b>) Sourgrass plants inoculated with isolate BSV2; (<b>B</b>) Sourgrass plants inoculated with isolate 46.</p>
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<p>Host-specificity evaluation of <span class="html-italic">A. panici-leucophaei.</span> Treated plants appearance at 90 days if age, 60 days after inoculation. (<b>A</b>) Non-inoculated (control) wheat; (<b>B</b>) wheat plants inoculated with the smut fungus; (<b>C</b>) sorghum control; (<b>D</b>) sorghum inoculated with the smut fungus; (<b>E</b>) rice control; and (<b>F</b>) rice inoculated with <span class="html-italic">A. panici-leucophaei</span>.</p>
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19 pages, 5019 KiB  
Article
The Dual Effect of Selenium Application in Reducing Fusarium Wilt Disease Incidence in Banana and Producing Se-Enriched Fruits
by Lina Liu, Chengye Wang, Kesuo Yin, Ming Ni, Yue Ding, Chengyun Li and Si-Jun Zheng
Plants 2024, 13(23), 3435; https://doi.org/10.3390/plants13233435 - 6 Dec 2024
Viewed by 706
Abstract
Fusarium wilt disease severely constrains the global banana industry. The highly destructive disease is caused by Fusarium oxysporum f. sp. cubense, especially its virulent tropical race 4 (Foc TR4). Selenium (Se), a non-essential mineral nutrient in higher plants, is known to [...] Read more.
Fusarium wilt disease severely constrains the global banana industry. The highly destructive disease is caused by Fusarium oxysporum f. sp. cubense, especially its virulent tropical race 4 (Foc TR4). Selenium (Se), a non-essential mineral nutrient in higher plants, is known to enhance plant resistance against several fungal pathogens. The experiments we conducted showed that selenium (≥10 mg/L) dramatically inhibited the growth of Foc TR4 mycelia and promoted plant growth. The further study we performed recorded a substantial reduction in the disease index (DI) of banana plants suffering from Foc TR4 when treated with selenium. The selenium treatments (20~160 mg/L) demonstrated significant control levels, with recorded symptom reductions ranging from 42.4% to 65.7% in both greenhouse and field trials. The DI was significantly negatively correlated with the total selenium content (TSe) in roots. Furthermore, selenium treatments enhanced the antioxidant enzyme activities of peroxidase (POD), polyphenol oxidase (PPO), and glutathione peroxidase (GSH-Px) in banana. After two applications of selenium (100 and 200 mg/plant) in the field, the TSe in banana pulps increased 23.7 to 25.9-fold and achieved the Se enrichment standard for food. The results demonstrate that selenium applications can safely augment root TSe levels, both reducing Fusarium wilt disease incidence and producing Se-enriched banana fruits. For the first time, this study has revealed that selenium can significantly reduce the damage caused by soil-borne pathogens in banana by increasing the activities of antioxidant enzymes and inhibiting fungal growth. Full article
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Graphical abstract

Graphical abstract
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<p>The effect of Se on banana plantlet growth. The graphs illustrate the effects on different concentrations of Se on banana plant height (<b>A</b>), the diameter of the pseudostem (<b>B</b>), and the number of leaves (<b>C</b>). Note: The data are presented as the mean ± Standard Error (SE), with <span class="html-italic">n</span> = 6 replicates. Statistical significance was determined between the treatment and the control using Student’s <span class="html-italic">t</span>-tests, with * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The effect of Se on banana plantlet growth. The graphs illustrate the effects on different concentrations of Se on banana plant height (<b>A</b>), the diameter of the pseudostem (<b>B</b>), and the number of leaves (<b>C</b>). Note: The data are presented as the mean ± Standard Error (SE), with <span class="html-italic">n</span> = 6 replicates. Statistical significance was determined between the treatment and the control using Student’s <span class="html-italic">t</span>-tests, with * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The effect of Se on the in vitro growth of <span class="html-italic">Foc</span> TR4 mycelia at 7 dpi. The graphs illustrate the colony diameter of <span class="html-italic">Foc</span> TR4 on PDA medium containing Se. Note: The negative control was PDA medium without Se. The positive control was PDA medium with 30% pyrazole ether fungicide suspension (10 mL/L), which is a broad-spectrum fungicide.</p>
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<p>The effect of Se in reducing <span class="html-italic">Foc</span> TR4 incidence in banana plants in greenhouse experiments. (<b>A</b>) Different above-ground symptoms of banana plants under Se treatments and inoculated with <span class="html-italic">Foc</span> TR4. Only images of plants treated with Se that exhibited significant reductions in DI with respect to the control are shown. (<b>B</b>) Different corm symptoms in banana plants under Se treatments and inoculated with <span class="html-italic">Foc</span> TR4. (<b>C</b>) The change in the <span class="html-italic">Foc</span> TR4 disease index of banana plants after treatment with different concentrations of Se. The data represent the mean ± SE, with 10 plants as replicates. Note: Se01–Se160 denote the Se treatment concentrations prior to <span class="html-italic">Foc</span> TR4 inoculation, whereas Se20B–Se160B signify the Se treatment concentrations post <span class="html-italic">Foc</span> TR4 inoculation. The letters a–d represent statistical significance determined using Student’s <span class="html-italic">t</span>-tests, and the absence of shared letters signifies a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>The effect of Se in reducing <span class="html-italic">Foc</span> TR4 incidence in banana plants in field experiments. (<b>A</b>) The effect of Se treatments in the field in reducing <span class="html-italic">Foc</span> TR4 incidence in banana plants during the fruiting stage. (<b>B</b>) The change in the <span class="html-italic">Foc</span> TR4 disease index of banana plants treated with different doses of Se. The data represent the mean ± SE, with 10 plants as replicates. The letters a, b represent the statistical significance determined using Student’s <span class="html-italic">t</span>-tests, and the absence of shared letters signifies a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level. Note: In (<b>A</b>), the plants labeled as C on the right of the banana plants treated with Se are the new suckers that emerged after the death of non-fruiting plants due to <span class="html-italic">Foc</span> TR4 infection.</p>
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<p>Changes in total Se content in different banana tissues after Se application. (<b>A</b>) The total Se content in different tissues of banana plantlets after Se application in greenhouse experiments. Data are presented as the mean ± SE, with <span class="html-italic">n</span> = 3 replicates. The letters a–f represent the statistical significance determined using Student’s <span class="html-italic">t</span>-tests, and the absence of shared letters signifies a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level. (<b>B</b>) Curvilinear correlation between DI and Se concentration in roots.</p>
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<p>The total Se content of fresh banana (peel and pulp) after Se application. Data are presented as the mean ± SE, with <span class="html-italic">n</span> = 9 replicates. The letters a–d represent the statistical significance determined using Student’s <span class="html-italic">t</span>-tests, and the absence of shared letters signifies a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Changes in antioxidant system indicators of eliminating ROS after Se and TR4 treatments, as well as POD activity (<b>A</b>), PPO activity (<b>B</b>), SOD activity (<b>C</b>), CAT activity (<b>D</b>), GSH-Px activity (<b>E</b>), and GSH concentration (<b>F</b>). Data are presented as the mean ± SE, with <span class="html-italic">n</span> = 5 replicates. The letters a–c represent the statistical significance determined using Student’s <span class="html-italic">t</span>-tests, and the absence of shared letters signifies a significant difference at the <span class="html-italic">p</span> &lt; 0.05 level. Note: CK: control; Se: banana leaves treated with Se for 21 d; ST7d: banana leaves treated with Se for 21 d and <span class="html-italic">Foc</span> TR4 for 7 d; ST14d: banana leaves treated with Se for 28 d and <span class="html-italic">Foc</span> TR4 for 14 d; ST21d: banana leaves treated with Se for 35 d and <span class="html-italic">Foc</span> TR4 for 21 d; ST28d: banana leaves treated with Se for 42 d and <span class="html-italic">Foc</span> TR4 for 28 d; ST42d: banana leaves treated with Se for 56 d and <span class="html-italic">Foc</span> TR4 for 42 d. The concentration of Se treatment was 40 mg/L.</p>
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