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Botryosphaeriaceae species are the major causal agents of walnut dieback worldwide, along with Diaporthe species. Botryosphaeria dothidea and Neofusicoccum parvum are the only two Botryosphaeriaceae species associated with this recently emergent disease in France, and little is known about their diversity, structure, origin and dispersion in French walnut orchards. A total of 381 isolates of both species were genetically typed using a sequence-based microsatellite genotyping (SSR-seq) method. This analysis revealed a low genetic diversity and a high clonality of these populations, in agreement with their clonal mode of reproduction. The genetic similarity among populations, regardless of the tissue type and the presence of symptoms, supports the hypothesis that these pathogens can move between fruits and twigs and display latent pathogen lifestyles. Contrasting genetic patterns between N. parvum populations from Californian and Spanish walnut orchards and the French ones suggested no conclusive evidence for pathogen transmission from infected materials. The high genetic similarity with French vineyards populations suggested instead putative transmission between these hosts, which was also observed with B. dothidea populations. Overall, this study provides critical insight into the epidemiology of two important pathogens involved in the emerging dieback of French walnut orchards, including their distribution, potential to mate, putative origin and disease pathways.

The crosstalk between clathrin-mediated endocytosis (CME) and the autophagy pathway has been reported in mammals; however, the interconnection of CME with autophagy has not been established in plants. Here, we report that the Arabidopsis CLATHRIN LIGHT CHAIN (CLC) subunit 2 and 3 double mutant, clc2-1 clc3-1, phenocopies Arabidopsis AUTOPHAGY-RELATED GENE (ATG) mutants in both autoimmunity and nutrient sensitivity. Accordingly, the autophagy pathway is significantly compromised in the clc2-1 clc3-1 mutant. Interestingly, multiple assays demonstrate that CLC2 directly interacts with ATG8h/ATG8i in a domain-specific manner. As expected, both GFP-ATG8h/GFP-ATG8i and CLC2-GFP are subjected to autophagic degradation, and degradation of GFP-ATG8h is significantly reduced in the clc2-1 clc3-1 mutant. Notably, simultaneous knockout of ATG8h and ATG8i by CRISPR-Cas9 results in enhanced resistance against Golovinomyces cichoracearum, supporting the functional relevance of the CLC2-ATG8h/8i interactions. In conclusion, our results reveal a link between the function of CLCs and the autophagy pathway in Arabidopsis.

Huanglongbing (HLB) is a fatal citrus disease that is currently threatening citrus varieties worldwide. One putative causative agent, Candidatus Liberibacter asiaticus (CLas), is vectored by Diaphorina citri, known as the Asian citrus psyllid (ACP). Understanding the details of CLas infection in HLB disease has been hindered by its Candidatus nature and the inability to confidently detect it in diseased trees during the asymptomatic stage. To identify early changes in citrus metabolism in response to inoculation of CLas using its natural psyllid vector, leaves from Madam Vinous sweet orange (Citrus sinensis (L.) Osbeck) trees were exposed to CLas-positive ACP or CLas-negative ACP and longitudinally analyzed using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry; data available in Dryad: 10.25338/B83H1Z), and metabolomics (proton nuclear magnetic resonance). At 4 weeks postexposure (wpe) to psyllids, the initial HLB plant response was primarily to the ACP and, to a lesser extent, the presence or absence of CLas. Additionally, analysis of 4, 8, 12, and 16 wpe identified 17 genes and one protein as consistently differentially expressed between leaves exposed to CLas-positive ACP versus CLas-negative ACP. This study informs identification of early detection molecular targets and contributes to a broader understanding of vector-transmitted plant pathogen interactions.

Saponins are plant secondary metabolites comprising glycosylated triterpenoids, steroids or steroidal alkaloids with a broad spectrum of toxicity to microbial pathogens and pest organisms that contribute to basal plant defense to biotic attack. Secretion of glycosyl hydrolases that enzymatically convert saponins into less toxic products was thus far the only mechanism reported to enable fungal pathogens to colonize their saponin-containing host plant(s). We studied the mechanisms that the fungus Botrytis cinerea utilizes to be tolerant to well-characterized, structurally related saponins from tomato and Digitalis purpurea. By gene expression studies, comparative genomics, enzyme assays and testing a large panel of fungal (knockout and complemented) mutants, we unraveled four distinct cellular mechanisms that participate in the mitigation of the toxic activity of these saponins and in virulence on saponin-producing host plants. The enzymatic deglycosylation that we identified is novel and unique to this fungus-saponin combination. The other three tolerance mechanisms operate in the fungal membrane and are mediated by protein families that are widely distributed in the fungal kingdom. We present a spatial and temporal model on how these mechanisms jointly confer tolerance to saponins and discuss the repercussions of these findings for other plant pathogenic fungi, as well as human pathogens.

BACKGROUND: Sclerotinia spp. are generalist fungal pathogens, infecting over 700 plant hosts worldwide, including major crops. While host resistance is the most sustainable and cost-effective method for disease management, complete resistance to Sclerotinia diseases is rare. We recently identified soft basal stem as a potential susceptibility factor to Sclerotinia minor infection in lettuce (Lactuca sativa) under greenhouse conditions. RESULTS: Analysis of stem and root cell wall composition in five L. sativa and one L. serriola accessions with varying growth habits and S. minor resistance levels revealed strong association between hemicellulose constituents, lignin polymers, disease phenotypes, and basal stem mechanical strength. Accessions resistant to basal stem degradation consistently exhibited higher levels of syringyl, guaiacyl, and xylose, but lower levels of fucose in stems. These findings suggest that stem cell wall polymers recalcitrant to breakdown by lignocellulolytic enzymes may contribute to stem strength-mediated resistance against S. minor. CONCLUSIONS: The lignin content, particularly guaiacyl and syringyl, along with xylose could potentially serve as biomarkers for identifying more resistant lettuce accessions and breeding lines. Basal stem degradation by S. minor was influenced by localized microenvironment conditions around the stem base of the plants.

BackgroundSoybean cyst nematodes (SCN) as animal parasites of plants are not usually interested in killing the host but are rather focused on completing their life cycle to increase population, resulting in substantial yield losses. Remarkably, some agricultural soils after long-term crop monoculture show a significant decline in SCN densities and suppress disease in a sustainable and viable manner. However, relatively little is known about the microbes and mechanisms operating against SCN in such disease-suppressive soils.ResultsGreenhouse experiments showed that suppressive soils (S) collected from two provinces of China and transplantation soils (CS, created by mixing 10% S with 90% conducive soils) suppressed SCN. However, SCN suppressiveness was partially lost or completely abolished when S soils were treated with heat (80 °C) and formalin. Bacterial community analysis revealed that the specific suppression in S and CS was mainly associated with the bacterial phylum Bacteroidetes, specifically due to the enrichment of Chitinophaga spp. and Dyadobacter sp., in the cysts. SCN cysts colonized by Chitinophaga spp. showed dramatically reduced egg hatching, with unrecognizable internal body organization of juveniles inside the eggshell due to chitinase activity. Whereas, Dyadobacter sp. cells attached to the surface coat of J2s increased soybean resistance against SCN by triggering the expression of defence-associated genes. The disease-suppressive potential of these bacteria was validated by inoculating them into conducive soil. The Dyadobacter strain alone or in combination with Chitinophaga strains significantly decreased egg densities after one growing cycle of soybeans. In contrast, Chitinophaga strains alone required more than one growing cycle to significantly reduce SCN egg hatching and population density.ConclusionThis study revealed how soybean monoculture for decades induced microbiota homeostasis, leading to the formation of SCN-suppressive soil. The high relative abundance of antagonistic bacteria in the cyst suppressed the SCN population both directly and indirectly. Because uncontrolled proliferation will likely lead to quick demise due to host population collapse, obligate parasites like SCN may have evolved to modulate virulence/proliferation to balance these conflicting needs. Video Abstract.

The unculturable bacterium 'Candidatus Liberibacter solanacearum' (CLso) is responsible for a growing number of emerging crop diseases. However, we know little about the diversity and ecology of CLso and its psyllid vectors outside of agricultural systems, which limits our ability to manage crop disease and understand the impacts this pathogen may have on wild plants in natural ecosystems. In North America, CLso is transmitted to crops by the native potato psyllid (Bactericera cockerelli). However, the geographic and host plant range of the potato psyllid and CLso beyond the borders of agriculture are not well understood. A recent study of historic herbarium specimens revealed that a unique haplotype of CLso was present infecting populations of the native perennial Solanum umbelliferum in California decades before CLso was first detected in crops. We hypothesized that this haplotype and other potentially novel CLso variants are still present in S. umbelliferum populations. To test this, we surveyed populations of S. umbelliferum in Southern California for CLso and potato psyllid vectors. We found multiple haplotypes of CLso and the potato psyllid associated with these populations, with none of these genetic variants having been previously reported in California crops. These results suggest that CLso and its psyllid vectors are much more widespread and diverse in North American natural plant communities than suggested by data collected solely from crops and weeds in agricultural fields. Further characterization of these apparently asymptomatic haplotypes will facilitate comparison with disease-causing variants and provide insights into the continued emergence and spread of CLso.

Plant pathogenic bacteria often have a narrow host range, which can vary among different isolates within a population. Here, we investigated the host range of the tomato pathogen Clavibacter michiganensis (Cm). We determined the genome sequences of 40 tomato Cm isolates and screened them for pathogenicity on tomato and eggplant. Our screen revealed that out of the tested isolates, five were unable to cause disease on any of the hosts, 33 were exclusively pathogenic on tomato, and two were capable of infecting both tomato and eggplant. Through comparative genomic analyses, we identified that the five non-pathogenic isolates lacked the chp/tomA pathogenicity island, which has previously been associated with virulence in tomato. In addition, we found that the two eggplant-pathogenic isolates encode a unique allelic variant of the putative serine hydrolase chpG (chpGC), an effector that is recognized in eggplant. Introduction of chpGC into a chpG inactivation mutant in the eggplant-non-pathogenic strain Cm101, failed to complement the mutant, which retained its ability to cause disease in eggplant and failed to elicit hypersensitive response (HR). Conversely, introduction of the chpG variant from Cm101 into an eggplant pathogenic Cm isolate (C48), eliminated its pathogenicity on eggplant, and enabled C48 to elicit HR. Our study demonstrates that allelic variation in the chpG effector gene is a key determinant of host range plasticity within Cm populations.

Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.

Fungi use the accessory gene content of their pangenomes to adapt to their environments. While gene presence-absence variation contributes to shaping accessory gene reservoirs, the genomic contexts that shape these events remain unclear. Since pangenome studies are typically species-wide and do not analyze different populations separately, it is yet to be uncovered whether presence-absence variation patterns and mechanisms are consistent across populations. Fungal plant pathogens are useful models for studying presence-absence variation because they rely on it to adapt to their hosts, and members of a species often infect distinct hosts. We analyzed gene presence-absence variation in the blast fungus, Magnaporthe oryzae (syn. Pyricularia oryzae), and found that presence-absence variation genes involved in host-pathogen and microbe-microbe interactions may drive the adaptation of the fungus to its environment. We then analyzed genomic and epigenomic features of presence-absence variation and observed that proximity to transposable elements, gene GC content, gene length, expression level in the host, and histone H3K27me3 marks were different between presence-absence variation genes and conserved genes. We used these features to construct a model that was able to predict whether a gene is likely to experience presence-absence variation with high precision (86.06%) and recall (92.88%) in M. oryzae. Finally, we found that presence-absence variation genes in the rice and wheat pathotypes of M. oryzae differed in their number and their genomic context. Our results suggest that genomic and epigenomic features of gene presence-absence variation can be used to better understand and predict fungal pangenome evolution. We also show that substantial intra-species variation can exist in these features.

The Pseudomonas syringae species complex is a heterogeneous group of plant pathogenic bacteria associated with a wide distribution of plant species. Advances in genomics are revealing the complex evolutionary history of this species complex and the wide array of genetic adaptations underpinning their diverse lifestyles. Here, we genomically characterize two P. syringae isolates collected from diseased Callery pears (Pyrus calleryana) in Berkeley, California in 2019 and 2022. We also isolated a lytic bacteriophage, which we characterized and evaluated for biocontrol efficiency. Using a multilocus sequence analysis and core genome alignment, we classified the P. syringae isolates as members of phylogroup 2, related to other strains previously isolated from Pyrus and Prunus. An analysis of effector proteins demonstrated an evolutionary conservation of effectoromes across isolates classified in PG2 and yet uncovered unique effector profiles for each, including the two newly identified isolates. Whole-genome sequencing of the associated phage uncovered a novel phage genus related to Pseudomonas syringae pv. actinidiae phage PHB09 and the Flaumdravirus genus. Finally, using in planta infection assays, we demonstrate that the phage was equally useful in symptom mitigation of immature pear fruit regardless of the Pss strain tested. Overall, this study demonstrates the diversity of P. syringae and their viruses associated with ornamental pear trees, posing spill-over risks to commercial pear trees and the possibility of using phages as biocontrol agents to reduce the impact of disease.IMPORTANCEGlobal change exacerbates the spread and impact of pathogens, especially in agricultural settings. There is a clear need to better monitor the spread and diversity of plant pathogens, including in potential spillover hosts, and for the development of novel and sustainable control strategies. In this study, we characterize the first described strains of Pseudomonas syringae pv. syringae isolated from Callery pear in Berkeley, California from diseased tissues in an urban environment. We show that these strains have divergent virulence profiles from previously described strains and that they can cause disease in commercial pears. Additionally, we describe a novel bacteriophage that is associated with these strains and explore its potential to act as a biocontrol agent. Together, the data presented here demonstrate that ornamental pear trees harbor novel P. syringae pv. syringae isolates that potentially pose a risk to local fruit production, or vice versa-but also provide us with novel associated phages, effective in disease mitigation.

Autophagy in eukaryotes functions to maintain homeostasis by degradation and recycling of long-lived and unwanted cellular materials. Autophagy plays important roles in pathogenicity of various fungal pathogens, suggesting that autophagy is a novel target for development of antifungal compounds. Here, we describe bioluminescence resonance energy transfer (BRET)-based high-throughput screening (HTS) strategy to identify compounds that inhibit fungal ATG4 cysteine protease-mediated cleavage of ATG8 that is critical for autophagosome formation. We identified ebselen (EB) and its analogs ebselen oxide (EO) and 2-(4-methylphenyl)-1,2-benzisothiazol-3(2H)-one (PT) as inhibitors of fungal pathogens Botrytis cinerea and Magnaporthe oryzae ATG4-mediated ATG8 processing. The EB and its analogs inhibit spore germination, hyphal development, and appressorium formation in Ascomycota pathogens, B. cinerea, M. oryzae, Sclerotinia sclerotiorum and Monilinia fructicola. Treatment with EB and its analogs significantly reduced fungal pathogenicity. Our findings provide molecular insights to develop the next generation of antifungal compounds by targeting autophagy in important fungal pathogens.

Theodor (Ted) Otto Diener (* 28 February 1921 in Zürich, Switzerland; † 28 March 2023 in Beltsville, MD, USA) pioneered research on viroids while working at the Plant Virology Laboratory, Agricultural Research Service, USDA, in Beltsville. He coined the name viroid and defined viroids important features like the infectivity of naked single-stranded RNA without protein-coding capacity. During scientific meetings in the 1970s and 1980s, viroids were often discussed at conferences together with other subviral pathogens. This term includes what are now called satellite RNAs and prions. Satellite RNAs depend on a helper virus and have linear or, in the case of virusoids, circular RNA genomes. Prions, proteinaceous infectious particles, are the agents of scrapie, kuru and some other diseases. Many satellite RNAs, like viroids, are non-coding and exert their function by thermodynamically or kinetically controlled folding, while prions are solely host-encoded proteins that cause disease by misfolding, aggregation and transmission of their conformations into infectious prion isoforms. In this memorial, we will recall the work of Ted Diener on subviral pathogens.

BACKGROUND: Fungal plant pathogens have dynamic genomes that allow them to rapidly adapt to adverse conditions and overcome host resistance. One way by which this dynamic genome plasticity is expressed is through effector gene loss, which enables plant pathogens to overcome recognition by cognate resistance genes in the host. However, the exact nature of these loses remains elusive in many fungi. This includes the tomato pathogen Cladosporium fulvum, which is the first fungal plant pathogen from which avirulence (Avr) genes were ever cloned and in which loss of Avr genes is often reported as a means of overcoming recognition by cognate tomato Cf resistance genes. A recent near-complete reference genome assembly of C. fulvum isolate Race 5 revealed a compartmentalized genome architecture and the presence of an accessory chromosome, thereby creating a basis for studying genome plasticity in fungal plant pathogens and its impact on avirulence genes. RESULTS: Here, we obtained near-complete genome assemblies of four additional C. fulvum isolates. The genome assemblies had similar sizes (66.96 to 67.78 Mb), number of predicted genes (14,895 to 14,981), and estimated completeness (98.8 to 98.9%). Comparative analysis that included the genome of isolate Race 5 revealed high levels of synteny and colinearity, which extended to the density and distribution of repetitive elements and of repeat-induced point (RIP) mutations across homologous chromosomes. Nonetheless, structural variations, likely mediated by transposable elements and effecting the deletion of the avirulence genes Avr4E, Avr5, and Avr9, were also identified. The isolates further shared a core set of 13 chromosomes, but two accessory chromosomes were identified as well. Accessory chromosomes were significantly smaller in size, and one carried pseudogenized copies of two effector genes. Whole-genome alignments further revealed genomic islands of near-zero nucleotide diversity interspersed with islands of high nucleotide diversity that co-localized with repeat-rich regions. These regions were likely generated by RIP, which generally asymmetrically affected the genome of C. fulvum. CONCLUSIONS: Our results reveal new evolutionary aspects of the C. fulvum genome and provide new insights on the importance of genomic structural variations in overcoming host resistance in fungal plant pathogens.

The Asian Citrus Psyllid (ACP), Diaphorina citri, is a vector of the pathological bacterium Candidatus Liberibacter asiaticus (CLas), which causes the most devastating disease to the citrus industry worldwide, known as greening or huanglongbing (HLB). Earlier field tests with an acetic acid-based lure in greening-free, Valencia citrus orange groves in California showed promising results. The same type of lures tested in São Paulo, Brazil, showed unsettling results. During the unsuccessful trials, we noticed a relatively large proportion of females in the field, ultimately leading us to test field-collected males and females for Wolbachia and CLas. The results showed high rates of Wolbachia and CLas infection in field populations. We then compared the olfactory responses of laboratory-raised, CLas-free, and CLas-infected males to acetic acid. As previously reported, CLas-uninfected males responded to acetic acid at 1 µg. Surprisingly, CLas-infected males required 50 × higher doses of the putative sex pheromone, thus explaining the failure to capture CLas-infected males in the field. CLas infection was also manifested in electrophysiological responses. Electroantennogram responses from CLas-infected ACP males were significantly higher than those obtained with uninfected males. To the best of our knowledge, this is the first report of a pathogen infection affecting a vectors response to a sex attractant.

BackgroundColletotrichum fungi infect a wide diversity of monocot and dicot hosts, causing diseases on almost all economically important plants worldwide. Colletotrichum is also a suitable model for studying gene family evolution on a fine scale to uncover events in the genome associated with biological changes.ResultsHere we present the genome sequences of 30 Colletotrichum species covering the diversity within the genus. Evolutionary analyses revealed that the Colletotrichum ancestor diverged in the late Cretaceous in parallel with the diversification of flowering plants. We provide evidence of independent host jumps from dicots to monocots during the evolution of Colletotrichum, coinciding with a progressive shrinking of the plant cell wall degradative arsenal and expansions in lineage-specific gene families. Comparative transcriptomics of 4 species adapted to different hosts revealed similarity in gene content but high diversity in the modulation of their transcription profiles on different plant substrates. Combining genomics and transcriptomics, we identified a set of core genes such as specific transcription factors, putatively involved in plant cell wall degradation.ConclusionsThese results indicate that the ancestral Colletotrichum were associated with dicot plants and certain branches progressively adapted to different monocot hosts, reshaping the gene content and its regulation.

Tomato spotted wilt orthotospovirus (TSWV) causes substantial economic loss to tomato production, and the Sw-5b resistance gene is widely deployed for management. Here, we show (i) the emergence of resistance-breaking (RB) TSWV strains in processing and fresh market tomato production in California over the past ten years, and (ii) evolutionary relationships with RB strains from other areas. A specific RT-PCR test was used to show the C118Y RB strain that emerged in Fresno County in 2016 quickly became predominant in the central production area and remained so through this study. In 2021, the C118Y strain was detected in the Northern production area, and was predominant in 2022. However, in 2023, the C118Y strain was unexpectedly detected in fewer spotted wilt samples from resistant varieties. This was due to emergence of the T120N RB strain, previously known to occur in Spain. A specific RT-PCR test was developed and used to show that the T120N RB strain was predominant in Colusa and Sutter counties (detected in 75-80% of samples), and detected in ~50% of samples from Yolo County. Pathogenicity tests confirmed California isolates of the T120N strain infected Sw-5b tomato varieties and induced severe symptoms. Another RB strain, C118F, was associated with spotted wilt samples of Sw-5 varieties from fresh market tomato production in southern California. Phylogenetic analyses with complete NSm sequences revealed that the C118Y and T120N RB strains infecting resistant processing tomato in California emerged locally, whereas those from fresh market production were more closely related to isolates from Mexico. Thus, widespread deployment of this single dominant resistance gene in California has driven the local emergence of multiple RB strains in different tomato production areas and types. These results further emphasize the need for ongoing monitoring for RB strains, and identification of sources of resistance to these strains.

The rice receptor kinase XA21 confers broad-spectrum resistance to Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of rice bacterial blight disease. To investigate the relationship between the expression level of XA21 and resulting resistance, we generated independent HA-XA21 transgenic rice lines accumulating the XA21 immune receptor fused with an HA epitope tag. Whole-genome sequence analysis identified the T-DNA insertion sites in sixteen independent T0 events. Through quantification of the HA-XA21 protein and assessment of the resistance to Xoo strain PXO99 in six independent transgenic lines, we observed that XA21-mediated resistance is dose dependent. In contrast, based on the four agronomic traits quantified in these experiments, yield is unlikely to be affected by the expression level of HA-XA21. These findings extend our knowledge of XA21-mediated defense and contribute to the growing number of well-defined genomic landing pads in the rice genome that can be targeted for gene insertion without compromising yield.

In most paddy plant diseases, the leaf is the primary source of information for image-based disease identification and classification. Image segmentation is an important step in the plant disease analysis process. It is used to separate the normal part of the leaf from the disease-affected part of the leaf. In this paper diseases like Bacterial leaf blight (BLB), Brown spot (BS), and Leaf smut (LS) are segmented using existing, K-means clustering, the Otsu thresholding method. Color space-based segmentation is newly proposed for paddy disease analysis. The intelligence of segmentation techniques is evaluated using the Error Rate (ER) and Overlap Rate (OR) across the three paddy diseases namely, BLB, BS, and LS. The results were compared among the Otsu, K-means and color thresholding segmentation techniques. The results revealed that the color thresholding method using the Lab model emerged as a novel segmentation method for all three paddy diseases with an average ER and OR of [0.36, 0.95]. The proposed work is carried out in the Department of Electronics and Communication research center at Ballari Institute of Technology and Management, Ballari, Karnataka during the period from August 2022 to February 2023 with the expert suggestions of the plant pathologist, from the University of Agricultural Science, Dharwad, Karnataka. [ABSTRACT FROM AUTHOR]

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. [ABSTRACT FROM AUTHOR]

Background: Fire blight, caused by Erwinia amylovora, is the most destructive bacterial disease affecting plants in the Rosaceae family, leading to significant economic losses. In Algeria, this disease has been reported since 2010. This study aimed to investigate the origin of fire blight in Algeria, in order to increase knowledge of the epidemiology of this serious disease and contribute to its management. A comprehensive characterization of 18 E. amylovora isolates recovered from northern Algeria between 2016 and 2021 to evaluate their phenotypical and genotypical diversity was conducted. Results: Phenotypic differences, particularly in growth kinetics, virulence, and fatty acid profiles, allowed differentiation of strains into five groups, possibly indicating distinct introduction events. Genetic characterization revealed that only one strain lacked the ubiquitous plasmid pEA29, which is correlated with reduced virulence, while none harbored the pEI70 plasmid. Phylogenetic analysis using concatenated sequences of the recA, groEL, rpoS, ams, and hrpN genes grouped Algerian strains with those from a broadly prevalent clade. CRISPR genotyping identified a novel CR1 pattern and three genotypes, two of them previously unreported. Conclusions: This study represents the first phenotypic, genetic, and phylogenetic investigation of E. amylovora strains in the region, and provides valuable information on the possible pathways of the introduction of this fire blight pathogen in northern Africa. The findings suggest one or more introduction events from a common ancestor, likely originating in northern Italy, followed by dispersal in various regions of Algeria. [ABSTRACT FROM AUTHOR]

Loop-Mediated Isothermal Amplification (LAMP) represents a valuable technique for DNA/RNA detection, known for its exceptional sensitivity, specificity, speed, accuracy, and affordability. This study focused on optimizing a LAMP-based method to detect early signs of Plasmopara halstedii, the casual pathogen of sunflower downy mildew, a severe threat to sunflower crops. Specifically, a set of six LAMP primers (two outer, two inner, and two loop) were designed from P. halstedii genomic DNA, targeting the ribosomal Large Subunit (LSU). These primers were verified by in silico analysis and experimental validation using both target and non-target species' DNAs. Optimizations encompassing reaction conditions (temperature, time) and component concentrations (magnesium, Bst DNA polymerase, primers, and dNTP) were determined. Validation of these optimizations was performed by agarose gel electrophoresis. Furthermore, various colorimetric chemicals (Neutral Red, Hydroxynaphthol Blue, SYBR Safe, Thiazole Green) were evaluated to facilitate method analysis, and the real-time analysis has been optimized, presenting multiple approaches for detecting sunflower downy mildew using the LAMP technique. The analytical sensitivity of the method was confirmed by detecting P. halstedii DNA concentrations as low as 0.5 pg/μl. This pioneering study, establishing P. halstedii detection through the LAMP method, stands as unique in its field. The precision, robustness, and practicality of the LAMP protocol make it an ideal choice for studies focusing on sunflower mildew, emphasizing its recommended use due to its operational ease and reliability. [ABSTRACT FROM AUTHOR]

Plant diseases are the major threats to sustainable agriculture, leading to yearly yield losses. Several phytopathogens such as fungi, bacteria and viruses are known to cause plant diseases that affect agricultural production. To overcome this, the use of toxic chemicals has increased, raising public concern due to their negative impacts on human health and the environment. An alternative to chemicals is the application of biological agents, which may include various bioactive compounds from microbes. Bacterial species’ non-ribosomal peptides (NRPs) are becoming promising alternatives to synthetic fungicides due to their antifungal activities. NRPs are small peptides usually containing special amino acids and other organic acids and are biosynthesised by non-ribosomal peptide synthetases (NRPSs). NRPs are used to control phytopathogens in fields. In this review, we highlighted the NRPs’ mechanisms of action against key fungal pathogens like Fusarium, Alternaria, and Botrytis. These mechanisms include disrupting cell membrane integrity, interfering with cell wall synthesis, and inhibiting essential cellular processes. We have also addressed various methods for utilising NRPs in agriculture for enhanced production, and formulation into biocontrol agents. Overall, NRPs offer significant potential to enhance crop resilience and reduce losses from fungal diseases, aligning with sustainable agriculture practices. However, further research and collaboration are needed to fully realise their potential in agricultural systems. [ABSTRACT FROM AUTHOR]

Background: In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. Method: The objective of this study is to develop a new deep learning‐based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network‐based enhanced kernel extreme learning machine (DBN‐EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN‐EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge‐based optimization algorithm (NBGSK). Result: The efficacy of the proposed DBN‐EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F‐measure. Conclusion: Experimental analyses demonstrate that the DBN‐EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: With the rapid development of deep learning, the recognition of rice disease images using deep neural networks has become a hot research topic. However, most previous studies only focus on the modification of deep learning models, while lacking research to systematically and scientifically explore the impact of different data sizes on the image recognition task for rice diseases. In this study, a functional model was developed to predict the relationship between the size of dataset and the accuracy rate of model recognition. RESULTS: Training VGG16 deep learning models with different quantities of images of rice blast‐diseased leaves and healthy rice leaves, it was found that the test accuracy of the resulting models could be well fitted with an exponential model (A = 0.9965 − e(−0.0603×I50−1.6693)). Experimental results showed that with an increase of image quantity, the recognition accuracy of deep learning models would show a rapid increase at first. Yet when the image quantity increases beyond a certain threshold, the accuracy of image classification would not improve much, and the marginal benefit would be reduced. This trend remained similar when the composition of the dataset was changed, no matter whether (i) the disease class was changed, (ii) the number of classes was increased or (iii) the image data were augmented. CONCLUSIONS: This study provided a scientific basis for the impact of data size on the accuracy of rice disease image recognition, and may also serve as a reference for researchers for database construction. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Early blight is one of the main diseases that causes serious pepper yield and quality reduction. The identification of the presymptomatic features during the incubation period is of great research significance, in that scientific prevention measures in the incubation period may effectively reduce the loss of peppers. RESULTS: The present study confirms the feasibility of the identification of presymptomatic features in pepper early blight by infrared thermography during the incubation period. The infrared thermal images of pepper blades were captured during the whole incubation period in our experiment. Evaluation parameters such as temperature uniformity (U) and the variation coefficient (C) were established by infrared thermal images for the blade temperature field. Evaluation parameters such as temperature uniformity U and variation coefficient C of pepper blades change during the time from being inoculated to significant morbidity. For cases of stabbing inoculation, there were no relatively obvious symptoms in visible light images until 96 h after inoculation, whereas some presymptomatic features appeared in infrared thermal images at 60 h after inoculation. Based on the uniformity changes (δU) and the variation coefficient changes (δC) in the temperature field distribution, the characteristic polynomial (CP)‐GBDT model has been established by optimizing the gradient boosting decision tree (GBDT) model. Compared with the GBDT mode, the CP‐GBDT model accuracy increased from 87.5% to 91.7% on the test set. CONCLUSION: Evaluation parameters such as temperature uniformity U and variation coefficient C can be used to effectively characterize the presymptomatic features of pepper early blight by infrared thermography during the incubation period. The results of the present study can provide a reference for the detection of crop diseases in the incubation period. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) mediate biological interactions and are produced by Pythium and Phytophthora species. These VOCs are biotechnologically relevant because the genera include important plant pathogens, whereby VOCs can aid in disease detection, and biological control agents, whereby VOCs contribute to disease control. Studies on VOC production, identification, and characterization of individual VOCs produced by Pythium and Phytophthora species are reviewed. VOCs detected in plants infected with Phytophthora species are also reviewed as potentially oomycete-derived VOCs. The Pythium- and Phytophthora-produced VOCs are compared with other microorganisms, and the main effects of these VOCs on microbial inhibition and plant-mediated effects are reviewed. These effects are summarized from direct demonstration studies and inferences based on the known functions of the identified Pythium- and Phytophthora-produced VOCs. There are two main applications of VOCs to plant disease control: the use of VOCs to detect pathogenic Pythium and Phytophthora species, e.g., e-nose detecting systems, and the use of VOC-producing biological control agents, e.g., Pythium oligandrum. Future research could understand how the VOCs are produced to engineer VOC levels in strains, analyze more oomycete species and strains, accurately quantify the VOCs produced, and exploit recent developments in analytical chemistry technology. Key points: • Compiled inventory of volatiles produced by Phytophthora and Pythium species • Volatilomes contain microbe-inhibiting and plant growth-promoting compounds • Volatile potential in disease detection and control supports analyzing more species [ABSTRACT FROM AUTHOR]

The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O‐ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O‐ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection. [ABSTRACT FROM AUTHOR]

AbstractLingonberry (Vaccinium vitis-idaea L.) is a dwarf shrub that is native to Northern climates. The berries are harvested from wild plants and the disease incidence in the crop is generally low. However, disease pressure may increase since the surface area dedicated to the crop is expected to expand in the future. In 2021 and 2022, brown necrotic leaves and stem dieback symptoms were observed on lingonberry plants grown at the St. John’s Research and Development Center, Newfoundland and Labrador, Canada. Pathogens were isolated from the diseased plant tissues and identified as Neopestalotiopsis rosae (n = 2) and Neopestalotiopsis zimbabwana (n = 2) using colony and conidia morphology as well as sequencing of the Internal Transcribed Spacer (ITS), beta-tubulin gene (ß-tub), and the translation elongation factor 1-alpha gene (tef1). Pathogenicity assays in detached lingonberry and strawberry leaves as well as lingonberry plants indicated that all four isolates were able to cause disease. This is the first report of N. rosae and N. zimbabwana causing leaf spot and stem dieback of lingonberry. Further monitoring of this disease is important to understand its prevalence in berry crops, as Neopestalotiopsis spp. in Canada have been reported to occur since 2018 in strawberry and other small fruits. Control measures and strategies are required to mitigate the potential risk from the disease, as there are no registered fungicides available to the growers. [ABSTRACT FROM AUTHOR]

Disease outbreaks are shaped by the 'disease triangle,' comprising a virulent pathogen, a susceptible plant, and appropriate environmental conditions. Recent advances have shed light on the intricate environment–plant–pathogen interactions. Rice resistance is manipulated by cytoplasmic and apoplastic effectors secreted by Magnaporthe oryzae. Environmental factors play a significant role in both blast disease epidemics and the regulation of host (or rice) resistance. Strategies such as protein engineering, genome editing, and host-induced gene silencing have been employed effectively to engineer blast-resistant rice plants. Rice blast is a highly destructive crop disease that requires the interplay of three essential factors: the virulent blast fungus, the susceptible rice plant, and favorable environmental conditions. Although previous studies have focused mainly on the pathogen and rice, recent research has shed light on the molecular mechanisms by which the blast fungus and environmental conditions regulate host resistance and contribute to blast disease outbreaks. This review summarizes significant achievements in understanding the sophisticated modulation of blast resistance by Magnaporthe oryzae effectors and the dual regulatory mechanisms by which environmental conditions influence rice resistance and virulence of the blast fungus. Furthermore, it emphasizes potential strategies for developing blast-resistant rice varieties to effectively control blast disease. [ABSTRACT FROM AUTHOR]

Cryptococcus neoformans and Cryptococcus gattii have been isolated from several different environmental sources, including several different tree species. This could indicate that the two species are ubiquitous in the same way as Saccharomyces cerevisiae. Laboratory infection studies have established that the two species can colonize the surface and inside of different plant species. However, the nature and number of studies make it difficult to form a general hypothesis about the relationship between C. neoformans/gattii and plants. Despite being reported, plant disease from infections is not well documented, and only from mated infection mixes. Interestingly, environmental isolates are often only of one of the two mating types. The profile of carbohydrate-active enzymes (CAZymes) encoded in the genomes of C. neoformans and C. gattii , makes it unlikely that they can degrade the plant cell wall as plant pathogens. Instead, they have the pectinolytic potential to live as commensal epi- or endophytes. While the opportunistic human pathogens Cryptococcus neoformans and Cryptococcus gattii are often isolated from plants and plant-related material, evidence suggests that these Cryptococcus species do not directly infect plants. Studies find that plants are important for Cryptococcus mating and dispersal. However, these studies have not provided enough detail about how plants and these fungi interact, especially in ways that could show the fungi are capable of causing disease. This review synthesizes recent findings from studies utilizing different plant models associated with the ecology of C. neoformans and C. gattii. Unanswered questions about their environmental role are highlighted. Overall, current research indicates that Cryptococcus utilizes plants as a substrate rather than harming them, arguing against Cryptococcus as a genuine plant pathogen. We hypothesize that plants represent reservoirs that aid dispersal, not hosts vulnerable to infection. [ABSTRACT FROM AUTHOR]

In past few years, salinity has become one of the important abiotic stresses in the agricultural fields due to anthropogenic activities. Salinity is leading towards yield losses due to soil infertility and increasing vulnerability of crops to diseases. Fluorescent pseudomonads are a diverse group of soil microorganisms known for promoting plant growth by involving various traits including protecting crops from infection by the phytopathogens. In this investigation, salt tolerant plant growth promoting bacterium Pseudomonas hunanensis SPT26 was selected as an antagonist against Fusarium oxysporum, causal organism of fusarium wilt in tomato. P. hunanensis SPT26 was found capable to produce various antifungal metabolites. Characterization of purified metabolites using Fourier transform infrared spectroscopy (FT-IR) and liquid chromatography-electron spray ionization-mass spectrometry (LC-ESI/MS) showed the production of various antifungal compounds viz., pyrolnitrin, pyochelin and hyroxyphenazine by P. hunanensis SPT26. In the preliminary examination, biocontrol activity of purified antifungal metabolites was checked by dual culture method and results showed 68%, 52% and 65% growth inhibition by pyrolnitrin, 1- hydroxyphenazine and the bacterium (P. hunanensis SPT26) respectively. Images from scanning electron microscopy (SEM) revealed the damage to the mycelia of fungal phytopathogen due to production of antifungal compounds secreted by P. hunanensis SPT26. Application of bioinoculant of P. hunanensis SPT26 and purified metabolites significantly decreased the disease incidence in tomato and increased the plant growth parameters (root and shoot length, antioxidant activity, number of fruits per plant, etc.) under saline conditions. The study reports a novel bioinoculant formulation with the ability to promote plant growth parameters in tomato in presence of phytopathogens even under saline conditions. [ABSTRACT FROM AUTHOR]

In this study, a combination of Serratia nematophila L2 and Bacillus velezensis W24 was used to biocontrol Sclerotinia sclerotiorum. When the mixed ratio of L2 to W24 was 1:1, the inhibition rate on the growth of S. sclerotiorum was 88.1 %. To gain a large number of bacteria, the culture medium and conditions were optimized. When the medium formula involved molasses (8.890 g/L), soy peptone (6.826 g/L), and NaCl (6.865 g/L), and the culture conditions were 32 °C, inoculum 4%, rotation speed 200 rpm, and pH 7, the maximum amounts of bacterial cells obtained. In order to prepare microcapsules, spray drying conditions were optimized. These conditions included the soluble starch concentration of 30 g/100 mL, the inlet air temperature of 160 °C, and the feed flow rate of 450 mL/h. Under these optimized conditions to prepare microcapsules, the mixed strain (L2 and W24) exhibited a survival rate of 93.9 ± 0.9% and a viable bacterial count of 6.4 × 1012 cfu/g. In addition, microcapsules (GW24Ms) which contained strains L2 and W24 had good storage stability. In the pot experiment, GW24Ms could effectively reduce the disease of soybean plants and the control effect was 88.4%. Thus, the microbial agent represents a promising biocontrol solution for managing Sclerotinia in soybean. • The GW24 (W24 and L2 strains were mixed) could inhibit the growth of Sclerotinia sclerotiorum. • The culture conditions and medium of GW24 were optimized. • The GW24 microcapsules were prepared by spray drying. • The GW24 microcapsules had great storage ability. • The GW24 microcapsules had great effects on S. sclerotiorum. [ABSTRACT FROM AUTHOR]

Recognition of leaf diseases in agriculture is considered a significant aspect of ensuring food quantity, quality, and production. In general, crop leaves are susceptible and fragile to various diseases such as leaf mold, target spot, late blight, bacterial spot or early blight of tomato plants. However, these tomato plant diseases are challenging to recognize, and early diagnosis is vital. At the same time, the continuous growth of convolutional neural network (CNN) approaches has significantly assisted plant disease diagnosis, providing a robust mechanism with highly accurate results. On the other hand, the number of unhealthy leaf images collected is often unbalanced, and diagnosing diseases with such an unbalanced data set is complicated. So, numerous models for tomato disease diagnosis based on CNN models have been proposed. However, none overcomes the class imbalance problem and, as a result, does not generate findings with impartial accuracy. This article presents an efficient and robust solution for the heterogeneous PYNQ-Z1 board. Optimization techniques-including loop unrolling, pipelining, array partitioning, and loop flattening-enhance the computation speed across the network's convolutional, fully connected, and max-pooling layers. The presented CNN approach comprises an 8-layer network termed MiniTomatoNet. This network is characterized by its streamlined structure, possessing only under 23 K parameters with all weights and biases and occupying a memory of 89.51 KB. In addition, the model trains with a re-weighted focal loss function and achieves 97.63% accuracy and 98.51% AUC score; the inference rate speed is 0.068 s per frame, and the power consumption is 2.35 W. Finally, the model is efficient, low power, robust, high accuracy and fast speed, making it a promising solution for diagnosing tomato diseases. [ABSTRACT FROM AUTHOR]

Calonectria leaf blight (CLB) is one of the best-known diseases of Eucalyptus spp., particularly in Asia and South America. Recently, typical symptoms of leaf and shoot blight caused by Calonectria spp. Were observed in a Eucalyptus plantation in the YunNan Province of southwestern China. Isolations were made from diseased leaves and top soil collected below the diseased trees to determine the causal agent of the disease and to consider the distribution characteristics of the Calonectria species. This resulted in 417 isolates, of which 228 were from leaves and 189 were from soil. Based on comparisons of DNA sequences for the act (actin), cmdA (calmodulin), his3 (histone H3), rpb2 (the second largest subunit of RNA polymerase), tef1 (translation elongation factor 1-alpha) and tub2 (β-tubulin) gene regions, as well as morphological characteristics, 11 Calonectria species were identified. These included Calonectria aciculata (0.7 %), Ca. colhounii (1.2 %), Ca. eucalypti (10.6 %) and Ca. honghensis (43.2 %) in the Ca. colhounii species complex, and Ca. aconidialis (15.3 %), Ca. asiatica (9.8 %), Ca. hongkongensis (1.0 %), Ca. ilicicola (6.0 %), Ca. kyotensis (0.5 %), and Ca. yunnanensis (11.3 %) in the Ca. kyotensis species complex. In addition, a novel species, accounting for 0.5 % of the isolates, was discovered and is described here as Ca. dianii sp. nov. in the Ca colhounii species complex. Most (99.1 %) of the isolates collected from the leaves resided in the Ca. colhounii species complex and a majority (95.8 %) of those from the soils were in Ca. kyotensis species complex. These results suggest that Calonectria spp. in the Ca. colhounii species complex infecting leaves might be adapted to that niche and that those in the Ca. kyotensis species complex are better adapted to a soil habitat. Calonectria leaf blight (CLB) is one of the best-known diseases of Eucalyptus spp., particularly in Asia and South America. A large number of Calonectria isolates were obtained from diseased leaves and soil samples collected from below the infected Eucalyptus trees in one plantation in China. A rich Calonectria diversity in the sampled Eucalyptus plantation were revealed. There were clear differences in species diversity and distribution relating to either the leaf or soil environments where they were found. It is becoming increasingly apparent that some species are predominantly soil inhabitants and different to those that cause plant diseases. [ABSTRACT FROM AUTHOR]

This article, titled "Measuring Heat Tolerance in a Sterilizing 'Anther-Smut' Pathogen of Wild Plants," explores the impact of increasing temperatures on the interaction between the herbaceous plant Silene latifolia and its fungal pathogen Microbotryum lychnidis-dioicae. The study investigates how temperature affects the growth, infection, and sporulation of the pathogen, as well as the survival and flowering of the host plant. The findings suggest that high temperatures suppress the life stages of the pathogen while not affecting the host, indicating a potential decline in the disease in the future. The article includes photographs illustrating the study and was published in the Bulletin of the Ecological Society of America. [Extracted from the article]

Cyclo (Phe-Pro) (cFP), a cyclic dipeptide with notable antifungal, antibacterial, and antiviral properties, shows great promise for biological control of plant diseases. Produced as a byproduct by non-ribosomal peptide synthetases (NRPS), the regulatory mechanism of cFP biosynthesis remains unclear. In a screening test of 997 Tn5 mutants of Burkholderia seminalis strain R456, we identified eight mutants with enhanced antagonistic effects against Fusarium graminearum (Fg). Among these, mutant 88's culture filtrate contained cFP, confirmed through HPLC and LC-MS, which actively inhibited Fg. The gene disrupted in mutant 88 is part of the Dct transport system (Dct-A, -B, -D), responsible for C4-dicarboxylate transport. Knockout mutants of Dct genes exhibited higher cFP levels than the wild type, whereas complementary strains showed no significant difference. Additionally, the presence of exogenous C4-dicarboxylates reduced cFP production in wild type R456, indicating that these substrates negatively regulate cFP synthesis. Given that cFP synthesis is related to NRPS, we previously identified an NRPS cluster in R456, horizontally transferred from algae. Specifically, knocking out gene 2061 within this NRPS cluster significantly reduced cFP production. A Fur box binding site was predicted upstream of gene 2061, and yeast one-hybrid assays confirmed Fur protein binding, which increased with additional C4-dicarboxylates. Knockout of the Fur gene led to up-regulation of gene 2061 and increased cFP production, suggesting that C4-dicarboxylates suppress cFP synthesis by enhancing Fur-mediated repression of gene 2061. [ABSTRACT FROM AUTHOR]

Trichothecenes are toxins produced by certain species from several fungal genera, including Aspergillus, Fusarium, Isaria, Paramyrothecium, Stachybotrys, Trichoderma, and Trichothecium. These toxins are of interest because they contribute to the toxigenicity, plant pathogenicity, and/or biological control activities of some fungi. All trichothecenes have the same core (12,13-epoxytrichothec-9-ene or EPT) structure but can differ from one another by the presence or absence of a macrocyclic ring formed from polyketide and isoprenoid substituents esterified to carbon atoms 4 and 15 of EPT, respectively. Genes required for formation and some modifications of EPT have been elucidated, but almost nothing is known about genes specific to the formation of the macrocyclic ring. Therefore, we used genomic, transcriptomic, metabolomic, and gene deletion analyses to identify genes that are required specifically for the formation of the macrocyclic ring. These analyses identified one gene, TRI24, that is predicted to encode an acyltransferase and that is required for macrocyclic ring formation during biosynthesis of macrocyclic trichothecenes by the fungus Paramyrothecium roridum. In addition, a TRI24 deletion mutant of P. roridum caused less severe disease symptoms on common bean and had less antifungal activity than its wild-type progenitor strain. We propose that the reduced aggressiveness and antifungal activity of the mutant resulted from its inability to produce trichothecenes with a macrocyclic ring. To our knowledge, this is the first report of a gene required specifically for the formation of the macrocyclic ring of trichothecenes and that loss of the macrocyclic ring of trichothecenes can alter the biological activities of a fungus. Key points: • TRI24 gene is found in all known macrocyclic trichothecene-producing fungi. • A tri24-deletion mutant exhibits a reduction in antifungal and plant disease activities. • TRI24 is the first described gene specific to macrocyclic trichothecene biosynthesis. [ABSTRACT FROM AUTHOR]

Plant growth‐promoting rhizobacteria (e.g., Bacillus) confer health benefits to the host. Bacillus siamensis has been used to control plant disease in fruits and vegetables, but its potential effects on crop performance remain unclear. This study investigated changes in the growth, root transcriptomic profile, and rhizosp here microbiome of maize plants (Zea may L.) following inoculation with B. siamensis 33. All inoculated plants grew better than non‐inoculated controls in pots, as exemplified by 24.0% and 6.8% increase in plant height and stem diameter, respectively (p < 0.01). Shoot dry weight also increased by 20.6% upon inoculation, accompanied by 46.9% increase in root dry weight (p < 0.01). Transcriptomic analysis revealed that inoculation induced (2.16‐ to 5.53‐fold) upregulated expression of genes related to auxin signal transduction in maize. The expression of genes involved in phenylpropanoid biosynthesis, glutathione metabolism, and plant defense‐related signal (e.g., jasmonic acid and salicylic acid) transduction was (2.06‐ to 11.30‐fold) upregulated in inoculated plants. Upon inoculation, bacterial α‐diversity trended higher and potentially beneficial taxa (e.g., Sphingobium, Flavihumibacter, and Parasegetibacter) were enriched in the rhizosphere, likely a result of enhanced root exudation of specific metabolites (e.g., flavonoids) and subsequent recruitment of beneficial microbial taxa. Potential microbial functions associated with nitrogen cycling and pollutant degradation were stimulated by inoculation. These findings indicate that B. siamensis 33 mediates the reprogramming of plant gene expression and reshaping of rhizosphere microbiome structure to bolster maize crop performance. [ABSTRACT FROM AUTHOR]

ABSTRACT Verticillium wilt caused by the soil‐borne fungus Verticillium dahliae Kleb., is a destructive plant disease that instigates severe losses in many crops. Improving plant resistance to Verticillium wilt has been a challenge in most crops. In this study, a V. dahliae secreted protein VdSP8 was identified and shown to activate hyper‐sensitive response (HR) and systemic acquired resistance (SAR) to Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) and Botrytis cinerea in tobacco plants. We identified a β‐glucosidase named GhBGLU46 as a cotton plant target of VdSP8. VdSP8 interacts with GhBGLU46 both in vivo and in vitro and promotes the β‐glucosidase activity of GhBGLU46. Silencing of GhBGLU46 reduced the expression of genes involved in lignin biosynthesis, such as GhCCR4, GhCCoAOMT2, GhCAD3 and GhCAD6, thus decreasing lignin deposition and increasing Verticillium wilt susceptibility. We have shown that GhBGLU46 is indispensable for the function of VdSP8 in plant resistance. These results suggest that plants have also evolved a strategy to exploit the invading effector protein VdSP8 to enhance plant resistance. [ABSTRACT FROM AUTHOR]

Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. [ABSTRACT FROM AUTHOR]

Medicinal plants have constituted a fundamental aspect of human health and wellness for millennia. The objective of this study was to document medicinal plants used to treat human and livestock ailments in the Tehuledere District. The data were collected using semistructured interviews, focus group discussions, and field observations with local informants. Preference ranking, direct matrix ranking, and informant consensus factor were used for data analysis. A total of 63 medicinal plant species belonging to 59 genera and 41 families were documented. The predominant families identified were Lamiaceae and Asteraceae, each containing 6 and 5 species, respectively. Of the recorded medicinal plants, 53 (80.95%) species were used for human ailments, whereas 12 (19.05%) species were used for animal health issues. Among the recorded medicinal plant species, shrubs constitute the highest number with 35% species. The most frequently used plant parts were leaves, accounting for 59% of remedies targeting human ailments. The administration of herbal treatments was primarily oral, aimed at addressing various diseases. The most significant threat to these medicinal plants was attributed to agricultural expansion, followed by the collection of firewood. The aim of documenting the use of medicinal plants in the treatment of diseases was to capture traditional practices, with species such as Eucalyptus globulus, Olea europaea subsp. cuspidata, and Lepidium sativum serving as the foundational basis for future pharmacological studies. It is imperative to prioritize the conservation of Laggera tomentosa and Urtica simensis to safeguard biodiversity and the cultural traditions associated with these endangered species. Engaging local communities in the management and conservation of plant resources, along with the preservation of their traditional knowledge, presents a cost‐effective and sustainable solution. [ABSTRACT FROM AUTHOR]

Precise and automatic plant disease detection represent a fundamental research topic. Indeed, traditional or manual disease detection can be laborious, inaccurate and time-consuming. In this work, a particular attention is given to automatic potato late blight disease detection. In fact, this devastating pathogen leads every year to a significant reduction in potato yields. In addition, since potatoes are a major food source for many nations, decreased production generate a real food insecurity. Therefore, considering these challenges, using advanced techniques in computer vision and machine learning allowed farmers to swiftly and accurately identify disease. The main objective of this research work is to generate a super-resolved labeled dataset (SRD) and evaluate its impact on plant disease detection. Three states of the art object detection methods (Faster-RCNN, Detr and Yolo V8) have been used to conduct an exhaustive evaluation on the effect of using a super-resolved dataset to perform detection. The obtained results show that the detection of potato late blight disease is enhanced using SRD. Training Yolo V8 model on SRD outperform other trained models in detecting very small lesions. In fact, training Yolo V8 on SRD reached higher mAP, lower Loss values and a reasonable inference time, making it suitable for real time applications. [ABSTRACT FROM AUTHOR]

In vitro co-culture techniques that allow the growth of plants and pathogens under controlled environmental conditions are being used to re-create host plant infection. These approaches reduce infection times, promote reproducibility, and enable a rapid evaluation of plant-pathogen interactions. As a result, these systems have become essential in breeding programs aimed at developing plant resistance to diseases. In this study, we developed and validated an in vitro co-culture system to investigate the Armillaria root rot (ARR) affecting Prunus spp. This disease, caused by fungi Armillaria spp. and Desarmillaria caespitosa, poses a severe threat to the stone and nut fruit industry due to the susceptibility of most commercial rootstocks to infection and the lack of effective management options for its control. The system consists of a fiber-supported liquid approach in sterile plastic vessels that allows a fast and reproducible fungal infection under controlled environmental conditions. The floor of the vessels was covered with a polyester-fiber matte and a germination paper that served as an interface between the mycelia and the plant roots. The vessels were subjected to inoculation with Armillaria mellea and D. caespitosa, and three Prunus genotypes ('Guardian®', 'MP-29', and Prunus cerasifera '14–4') were co-cultured with both fungi. Disease progression and plant and fungal biomass were monitored during co-culture. The presented in vitro co-culture approach facilitates the concurrent growth of Armillaria/Desarmillaria spp. and Prunus spp., excluding most of the limitations associated with greenhouses and field experiments. This system provides consistent and reproducible conditions for investigating a prominent plant disease affecting Prunus spp. [ABSTRACT FROM AUTHOR]

A significant population of biocontrol microorganisms resides in the rhizosphere of plants, which can be utilized for plant disease control. To explore the potential of rhizosphere soil microorganisms as biocontrol agents against pepper blight, a bacterial strain Pa608 was screened from rhizosphere soil of pepper and identified as Pseudomonas aeruginosa through morphological characteristics and 16S rRNA sequences. The result showed that the strain Pa608 demonstrated antagonistic activity against Phytophthora capsici, effectively suppressing mycelial growth. The potted experiment showed a high control efficacy of 88.0%. Remarkably, the strain Pa608 also reduced the disease index of pepper blight in the field, resulting in control efficiencies of 74.9%. Moreover, the strain Pa608 also enhanced pepper plant height and yield. GC-MS analysis revealed the production of numerous secondary metabolites by the strain Pa608, with α-pinene displaying potent anti-oomycete activity by inhibiting P. capsici growth. In conclusion, P. aeruginosa Pa608 exhibited high biocontrol activity against P. capsici and can be utilized for the management of P. capsici in pepper cultivation. [ABSTRACT FROM AUTHOR]

Fungi play critical roles in the homeostasis of ecosystems globally and have emerged as significant causes of an expanding repertoire of devastating diseases in plants, animals, and humans. In this Commentary, we highlight the importance of fungal pathogens and argue for concerted research efforts to enhance understanding of fungal virulence, antifungal immunity, novel drug targets, antifungal resistance, and the mycobiota to improve human health. [ABSTRACT FROM AUTHOR]

Enzymes are molecules that play a crucial role in maintaining homeostasis and balance in all living organisms by catalyzing metabolic and cellular processes. If an enzyme's mechanism of action is inhibited, the progression of certain diseases can be slowed or halted, making enzymes a key therapeutic target. Therefore, identifying or developing enzyme inhibitors is essential for treating significant diseases and ensuring plant defense against pathogens. This review aims to compile information on various types of enzyme inhibitors, particularly those that are well studied and beneficial in both human and plant contexts, by analyzing their mechanisms of action and the resulting benefits. Specifically, this review focuses on three different types of enzyme inhibitors that are most studied, recognized, and cited, each with distinct areas of action and potential benefits. For instance, serine enzyme inhibitors in plants help defend against pathogens, while the other two classes—alpha-glucosidase inhibitors and carbonic anhydrase inhibitors—have significant effects on human health. Furthermore, this review is also intended to assist other researchers by providing valuable insights into the biological effects of specific natural or synthetic inhibitors. Based on the current understanding of these enzyme inhibitors, which are among the most extensively studied in the scientific community, future research could explore their use in additional applications or the development of synthetic inhibitors derived from natural ones. Such inhibitors could aid in defending against pathogenic organisms, preventing the onset of diseases in humans, or even slowing the growth of certain pathogenic microorganisms. Notably, carbonic anhydrase inhibitors have shown promising results in potentially replacing antibiotics, thereby addressing the growing issue of antibiotic resistance. [ABSTRACT FROM AUTHOR]

Bacterial virulence plays an important role in infection. Antibacterial virulence factors are effective for preventing crop bacterial diseases. Resin acid copper salt as an effective inhibitor exhibited excellent anti-Xanthomonas oryzae pv. oryzae (Xoo) activity with an EC50 of 50.0 μg mL−1. Resin acid copper salt (RACS) can reduce extracellular polysaccharides' (EPS's) biosynthesis by down-regulating gumB relative expression. RACS can also effectively inhibit the bio-mass of Xoo biofilm. It can reduce the activity of Xoo extracellular amylase at a concentration of 100 μg mL−1. Meanwhile, the results of virtual computing suggested that RACS is an enzyme inhibitor. RACS displayed good curative activity with a control effect of 38.5%. Furthermore, the result of the phytotoxicity assessment revealed that RACS exhibited slight toxicity compared with the control at a concentration of 200 μg mL−1. The curative effect was increased to 45.0% using an additional antimicrobial agent like orange peel essential oil. RACS markedly inhibited bacterial pathogenicity at a concentration of 100 μg mL−1 in vivo. [ABSTRACT FROM AUTHOR]

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure. [ABSTRACT FROM AUTHOR]

Featured Application: The SMARTerra decision support system provides high-resolution risk maps related to plant diseases and pests, enabling prompt and targeted intervention that minimizes environmental impact while minimizing economic losses. The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis. [ABSTRACT FROM AUTHOR]