Pathogen protein modularity enables elaborate mimicry of a host phosphatase.

Pathogens produce diverse effector proteins to manipulate host cellular processes. However, how functional diversity is generated in an effector repertoire is poorly understood. Many effectors in the devastating plant pathogen Phytophthora contain tandem repeats of the "(L)WY" motif, which are structurally conserved but variable in sequences. Here, we discovered a functional module formed by a specific (L)WY-LWY combination in multiple Phytophthora effectors, which efficiently recruits the serine/threonine protein phosphatase 2A (PP2A) core enzyme in plant hosts. Crystal structure of an effector-PP2A complex shows that the (L)WY-LWY module enables hijacking of the host PP2A core enzyme to form functional holoenzymes. While sharing the PP2A-interacting module at the amino terminus, these effectors possess divergent C-terminal LWY units and regulate distinct sets of phosphoproteins in the host. Our results highlight the appropriation of an essential host phosphatase through molecular mimicry by pathogens and diversification promoted by protein modularity in an effector repertoire.

Hiding in plain sight: Genome-wide recombination and a dynamic accessory genome drive diversity in Fusarium oxysporum f.sp. ciceris.

Understanding the origins of variation in agricultural pathogens is of fundamental interest and practical importance, especially for diseases that threaten food security. Fusarium oxysporum is among the most important of soil-borne pathogens, with a global distribution and an extensive host range. The pathogen is considered to be asexual, with horizontal transfer of chromosomes providing an analog of assortment by meiotic recombination. Here, we challenge those assumptions based on the results of population genomic analyses, describing the pathogen's diversity and inferring its origins and functional consequences in the context of a single, long-standing agricultural system. We identify simultaneously low nucleotide distance among strains, and unexpectedly high levels of genetic and genomic variability. We determine that these features arise from a combination of genome-scale recombination, best explained by widespread sexual reproduction, and presence-absence variation consistent with chromosomal rearrangement. Pangenome analyses document an accessory genome more than twice the size of the core genome, with contrasting evolutionary dynamics. The core genome is stable, with low diversity and high genetic differentiation across geographic space, while the accessory genome is paradoxically more diverse and unstable but with lower genetic differentiation and hallmarks of contemporary gene flow at local scales. We suggest a model in which episodic sexual reproduction generates haplotypes that are selected and then maintained through clone-like dynamics, followed by contemporary genomic rearrangements that reassort the accessory genome among sympatric strains. Taken together, these processes contribute unique genome content, including reassortment of virulence determinants that may explain observed variation in pathogenic potential.

Meta-learning shows great potential in plant disease recognition under few available samples.

Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta-learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

An NBS-LRR protein in the Rpp1 locus negates the dominance of Rpp1-mediated resistance against Phakopsora pachyrhizi in soybean.

The soybean Rpp1 locus confers resistance to Phakopsora pachyrhizi, causal agent of rust, and resistance is usually dominant over susceptibility. However, dominance of Rpp1-mediated resistance is lost when a resistant genotype (Rpp1 or Rpp1b) is crossed with susceptible line TMG06_0011, and the mechanism of this dominant susceptibility (DS) is unknown. Sequencing the Rpp1 region reveals that the TMG06_0011 Rpp1 locus has a single nucleotide-binding site leucine-rich repeat (NBS-LRR) gene (DS-R), whereas resistant PI 594760B (Rpp1b) is similar to PI 200492 (Rpp1) and has three NBS-LRR resistance gene candidates. Evidence that DS-R is the cause of DS was reflected in virus-induced gene silencing of DS-R in Rpp1b/DS-R or Rpp1/DS-R heterozygous plants with resistance partially restored. In heterozygous Rpp1b/DS-R plants, expression of Rpp1b candidate genes was not significantly altered, indicating no effect of DS-R on transcription. Physical interaction of the DS-R protein with candidate Rpp1b resistance proteins was supported by yeast two-hybrid studies and in silico modeling. Thus, we conclude that suppression of resistance most likely does not occur at the transcript level, but instead probably at the protein level, possibly with Rpp1 function inhibited by binding to the DS-R protein. The DS-R gene was found in other soybean lines, with an estimated allele frequency of 6% in a diverse population, and also found in wild soybean (Glycine soja). The identification of a dominant susceptible NBS-LRR gene provides insight into the behavior of NBS-LRR proteins and serves as a reminder to breeders that the dominance of an R gene can be influenced by a susceptibility allele.

Order of arrival and nutrient supply alter outcomes of co-infection with two fungal pathogens.

A pathogen arriving on a host typically encounters a diverse community of microbes that can shape priority effects, other within-host interactions and infection outcomes. In plants, environmental nutrients can drive trade-offs between host growth and defence and can mediate interactions between co-infecting pathogens. Nutrients may thus alter the outcome of pathogen priority effects for the host, but this possibility has received little experimental investigation. To disentangle the relationship between nutrient availability and co-infection dynamics, we factorially manipulated the nutrient availability and order of arrival of two foliar fungal pathogens (Rhizoctonia solani and Colletotrichum cereale) on the grass tall fescue (Lolium arundinaceum) and tracked disease outcomes. Nutrient addition did not influence infection rates, infection severity or plant biomass. Colletotrichum cereale facilitated R. solani, increasing its infection rate regardless of their order of inoculation. Additionally, simultaneous and C. cereale-first inoculations decreased plant growth and-in plants that did not receive nutrient addition-increased leaf nitrogen concentrations compared to uninoculated plants. These effects were partially, but not completely, explained by the duration and severity of pathogen infections. This study highlights the importance of understanding the intricate associations between the order of pathogen arrival, host nutrient availability and host defence to better predict infection outcomes.

From glycans to green biotechnology: exploring cell wall dynamics and phytobiota impact in plant glycopathology.

Filamentous plant pathogens, including fungi and oomycetes, pose significant threats to cultivated crops, impacting agricultural productivity, quality and sustainability. Traditionally, disease control heavily relied on fungicides, but concerns about their negative impacts motivated stakeholders and government agencies to seek alternative solutions. Biocontrol agents (BCAs) have been developed as promising alternatives to minimize fungicide use. However, BCAs often exhibit inconsistent performances, undermining their efficacy as plant protection alternatives. The eukaryotic cell wall of plants and filamentous pathogens contributes significantly to their interaction with the environment and competitors. This highly adaptable and modular carbohydrate armor serves as the primary interface for communication, and the intricate interplay within this compartment is often mediated by carbohydrate-active enzymes (CAZymes) responsible for cell wall degradation and remodeling. These processes play a crucial role in the pathogenesis of plant diseases and contribute significantly to establishing both beneficial and detrimental microbiota. This review explores the interplay between cell wall dynamics and glycan interactions in the phytobiome scenario, providing holistic insights for efficiently exploiting microbial traits potentially involved in plant disease mitigation. Within this framework, the incorporation of glycobiology-related functional traits into the resident phytobiome can significantly enhance the plant's resilience to biotic stresses. Therefore, in the rational engineering of future beneficial consortia, it is imperative to recognize and leverage the understanding of cell wall interactions and the role of the glycome as an essential tool for the effective management of plant diseases.

MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks.

Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system's model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.

Automated grape leaf nutrition deficiency disease detection and classification Equilibrium Optimizer with deep transfer learning model.

Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification utilising a Stacked Autoencoder (SAE) model. Each of these processes is carried out in a series of separate steps. During the image preprocessing stage, contrast limited adaptive histogram equalisations (CLAHE) is utilized to improve the contrasts, and Adaptive Bilateral Filtering (ABF) to get rid of any noise that may be present. The SqueezeNet paradigm is utilized to obtain relevant characteristics from the pictures that have been preprocessed, and the EO technique is utilized to fine-tune the hyperparameters. Finally, the SAE model categorises the diseases that affect the grape leaf. The simulation analysis of the EODTL-GLDC technique tested New Plant Diseases Datasets and the results were inspected in many prospects. The results demonstrate that this model outperforms other deep learning techniques and methods that are more often related to machine learning. Specifically, this technique was able to attain a precision of 96.31% on the testing datasets and 96.88% on the training data set that was split 80:20. These results offer more proof that the suggested strategy is successful in automating the detection and categorization of grape leaf diseases.

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

Biocontrol potential of plant growth-promoting rhizobacteria against plant disease and insect pest.

Biological control is a promising approach to enhance pathogen and pest control to ensure high productivity in cash crop production. Therefore, PGPR biofertilizers are very suitable for application in the cultivation of tea plants (Camellia sinensis) and tobacco, but it is rarely reported so far. In this study, production of a consortium of three strains of PGPR were applied to tobacco and tea plants. The results demonstrated that plants treated with PGPR exhibited enhanced resistance against the bacterial pathogen Pseudomonas syringae (PstDC3000). The significant effect in improving the plant's ability to resist pathogen invasion was verified through measurements of oxygen activity, bacterial colony counts, and expression levels of resistance-related genes (NPR1, PR1, JAZ1, POD etc.). Moreover, the application of PGPR in the tea plantation showed significantly reduced population occurrences of tea green leafhoppers (Empoasca onukii Matsuda), tea thrips (Thysanoptera:Thripidae), Aleurocanthus spiniferus (Quaintanca) and alleviated anthracnose disease in tea seedlings. Therefore, PGPR biofertilizers may serve as a viable biological control method to improve tobacco and tea plant yield and quality. Our findings revealed part of the mechanism by which PGPR helped improve plant biostresses resistance, enabling better application in agricultural production.

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

CRISPRals: A Web Database for Assessing the CRISPR Defense System in the Ralstonia solanacearum Species Complex to Avoid Phage Resistance.

Clustered regularly interspaced short palindromic repeats (CRISPR) has been widely characterized as a defense system against phages and other invading elements in bacteria and archaea. A low percentage of Ralstonia solanacearum species complex (RSSC) strains possess the CRISPR array and the CRISPR-associated proteins (Cas) that would confer immunity against various phages. To provide a wide-range screen of the CRISPR presence in the RSSC, we analyzed 378 genomes of RSSC strains to find the CRISPR locus. We found that 20.1, 14.3, and 54.5% of the R. solanacearum, R. pseudosolanacearum, and R. syzygii strains, respectively, possess the CRISPR locus. In addition, we performed further analysis to identify the respective phages that are restricted by the CRISPR arrays. We found 252 different phages infecting different strains of the RSSC, by means of the identification of similarities between the protospacers in phages and spacers in bacteria. We compiled this information in a database with web access called CRISPRals (https://crisprals.yachaytech.edu.ec/). Additionally, we made available a number of tools to detect and identify CRISPR array and Cas genes in genomic sequences that could be uploaded by users. Finally, a matching tool to relate bacteria spacer with phage protospacer sequences is available. CRISPRals is a valuable resource for the scientific community that contributes to the study of bacteria-phage interaction and a starting point that will help to design efficient phage therapy strategies.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

A Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2022.

This scientometric study reviews the scientific literature and CABI distribution records published in 2022 to find evidence of major disease outbreaks and first reports of pathogens in new locations or on new hosts. This is the second time we have done this, and this study builds on our work documenting and analyzing reports from 2021. Pathogens with three or more articles identified in 2022 literature were Xylella fastidiosa, Bursaphelenchus xylophilus, Meloidogyne species complexes, 'Candidatus Liberibacter asiaticus', Raffaelea lauricola, Fusarium oxysporum formae specialis, and Puccinia graminis f. sp. tritici. Our review of CABI distribution records found 29 pathogens with confirmed first reports in 2022. Pathogens with four or more first reports were Meloidogyne species complexes, Pantoea ananatis, grapevine red globe virus, and Thekopsora minima. Analysis of the proportion of new distribution records from 2022 indicated that grapevine red globe virus, sweet potato chlorotic stunt virus, and 'Ca. Phytoplasma vitis' may have been actively spreading. As we saw last year, there was little overlap between the pathogens identified by reviewing scientific literature versus distribution records. We hypothesize that this lack of concordance is because of the unavoidable lag between first reports of the type reported in the CABI database of a pathogen in a new location and any subsequent major disease outbreaks being reported in the scientific literature, particularly because the latter depends on the journal policy on types of papers to be considered, whether the affected crop is major or minor, and whether the pathogen is of current scientific interest. Strikingly, too, there was also no overlap between species assessed to be actively spreading in this year's study and those identified last year. We hypothesize that this is because of inconsistencies in sampling coverage and effort over time and delays between the first arrival of a pathogen in a new location and its first report, particularly for certain classes of pathogens causing only minor or non-economically damaging symptoms, which may have been endemic for some time before being reported. In general, introduction of new pathogens and outbreaks of extant pathogens threaten food security and ecosystem services. Continued monitoring of these threats is essential to support phytosanitary measures intended to prevent pathogen introductions and management of threats within a country.

What's in my Pot? Six Colletotrichum Species Causing Anthracnose in Brazilian Pecan Orchards.

Pecan (Carya illinoinensis) is one important exotic forest crop cultivated in South America, specifically in Brazil, Uruguay, and Argentina. However, diseases such as anthracnose, favored by high humidity conditions and high summer temperatures, make its cultivation difficult, causing important loss to pecan farmers. This study used morphological and molecular approaches to identify the Colletotrichum species causing anthracnose in pecan plantations in Southern Brazil. The isolates obtained from pecan fruits with anthracnose symptoms were grouped through quantitative morphological characteristics into three distinct morphotypes. Molecular analysis of nuclear genes allowed the identification of six species of Colletotrichum causing anthracnose in pecan: C. nymphaeae, C. fioriniae, C. gloeosporioides, C. siamense, C. kahawae, and C. karsti. Three of these species are reported for the first time as causal agents of anthracnose in pecan. Therefore, these results provide an important basis for the adoption and/or development of anthracnose management strategies in pecan orchards cultivated in southern Brazil and neighboring countries.

The persistent threat of emerging plant disease pandemics to global food security.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Dynamic localization of a helper NLR at the plant-pathogen interface underpins pathogen recognition.

Plants employ sensor-helper pairs of NLR immune receptors to recognize pathogen effectors and activate immune responses. Yet, the subcellular localization of NLRs pre- and postactivation during pathogen infection remains poorly understood. Here, we show that NRC4, from the "NRC" solanaceous helper NLR family, undergoes dynamic changes in subcellular localization by shuttling to and from the plant-pathogen haustorium interface established during infection by the Irish potato famine pathogen Phytophthora infestans. Specifically, prior to activation, NRC4 accumulates at the extrahaustorial membrane (EHM), presumably to mediate response to perihaustorial effectors that are recognized by NRC4-dependent sensor NLRs. However, not all NLRs accumulate at the EHM, as the closely related helper NRC2 and the distantly related ZAR1 did not accumulate at the EHM. NRC4 required an intact N-terminal coiled-coil domain to accumulate at the EHM, whereas the functionally conserved MADA motif implicated in cell death activation and membrane insertion was dispensable for this process. Strikingly, a constitutively autoactive NRC4 mutant did not accumulate at the EHM and showed punctate distribution that mainly associated with the plasma membrane, suggesting that postactivation, NRC4 may undergo a conformation switch to form clusters that do not preferentially associate with the EHM. When NRC4 is activated by a sensor NLR during infection, however, NRC4 forms puncta mainly at the EHM and, to a lesser extent, at the plasma membrane. We conclude that following activation at the EHM, NRC4 may spread to other cellular membranes from its primary site of activation to trigger immune responses.

First report of Colletotrichum queenslandicum associated with chilli anthracnose in Mauritius.

Globally, chilli (Capsicum annuum L.) is one of the most economically important and widely cultivated crop which elicits ethnomedicinal and nutritional potential as well as enhancing the taste and aroma of foods (Ayob et al., 2022; Kiran et al., 2020). Anthracnose disease is regarded as a prime constraint in chilli production, leading to enormous losses in tropical and subtropical countries. In September 2022, chilli fruit displaying sunken, shriveled and dark bown to black lesion with abundant acervuli on the surface was obtained from Flacq, Mauritius. From the symptomatic tissue, small pieces of the diseased tissue were excised, surface-disinfected using 1% sodium hypochlorite, twice rinsed using sterilized distilled water, air-dried and plated on PDA. After 7 days of incubation at room temperature, white to greyish white colony with dense white cottony aerial mycelium was recovered. Out of two isolates, CHF and CH10, the latter was considered for morphological and molecular characterization. The observed conidia (n=30) were unicellular, straight, cylindrical with rounded ends and slight constriction near the centre and had average length and width of 20.5 µm and 6 µm, respectively. For growth rate measurement of the isolate, two 5×5 mm of fungal agar plugs were taken from growing edge of colony, inoculated at centre of individual PDA plate and incubated at room temperature with a natural light/dark cycle. The diameter of the cultures were measured perpendicularly for a period of 7 days and the growth rate was calculated as 7-day average of daily growth (mm day-1). The growth rate of the fungal isolate (CH10) was 13.5 mm day-1 on PDA. Based on the morphological characters, the isolate was classified within the C. gloeosporioides species complex. For precise identification of the isolate, DNA was extracted from fungal mycelium using traditional DNA isolation methods (Ranghoo and Hyde, 2000), followed by PCR amplification and DNA sequencing using primer pairs ITS4/ITS5 (White et al., 1990), GDF/GDR and T1/Bt2b (Gan et al., 2016), respectively. ITS gene sequence (600 bp) confirmed that the isolate was Colletotrichum, with 99.83% similarity to KR704204 while GADPH (277 bp), TUB2 (733 bp) and ApMat (801 bp) gene sequences showed 99.64 to 100% similarity to C. queenslandicum with GenBank reference sequences, KT372374, KU221378 and MG674932 respectively. The gene sequences of isolate CH10 were deposited in GenBank database under the following accession numbers OR681557 (ITS), OR233734 (GADPH), OR475575 (TUB2) and PP622748 (ApMat). Koch's postulates were confirmed by spraying disease-free chilli plants with 10µL of conidial suspension (1 × 106 spores/ml) prepared from 7 days old colony of isolate CH10. Healthy chilli plants inoculated with sterile distilled water served as a negative control experiment. The plants were grown in pots in a moist chamber at 25˚C. After 5 days post-inoculation, anthracnose symptoms were developed on test plants while the control plant remained asymptomatic. The original isolate was successfully recovered from the test fruits, thus fulfilling Koch's postulates. The experiment was repeated thrice and revealed the same results. To the best of our knowledge, this is the first record of C. queenslandicum in Mauritius and is the first time to report anthracnose of chilli caused by this fungus. Colletotrichum queenslandicum has previously been reported in Europe, Mexico, US, Puerto Rico, Australia, Fiji, Brazil, Indonesia and China. Furthermore, the latter was associated with papaya, avocado, cashew, coffee, Persian lime, Licania tomentosa, white mangrove, lychee, mango, Nephelium lappaceum, olive, passionfruit, Dracaena cambodiana and Syzygium australe (Câmara and Vieira, 2022; Shidiq et al., 2024; Wang et al., 2022). This study will allow local farmers training and extension facilities to increase awareness among farmers about this disease-causing agent and allow them to take necessary measures for building up chilli crops resilience against this new and emerging pathogen in Mauritius.

First Report of Phyllosticta capitalensis Causing Postharvest Brown Spot Disease on Guava Fruit in Thailand.

Guava (Psidium guajava L.) is a popular fruit crop that is widely cultivated in Thailand. In November 2023, brown spot disease on guava was observed during postharvest storage at 22 to 31°C and 70 to 75% relative humidity over a period of 3 to 7 days in Fang District, Chiang Mai Province, Thailand. The disease incidence was ~20% of 100 fruits per pallet box. The disease severity on each fruit ranged from 40 to 70% of the surface area affected by lesions. The symptoms appeared as circular to irregular brown to dark brown spots, ranging from 5 to 30 mm in diameter. Fungi were isolated from lesions using a single conidial isolation method (Choi et al. 1999). Two fungal isolates (SDBR-CMU497 and SDBR-CMU498) with similar morphology were obtained. Colonies on potato dextrose agar (PDA) and malt extract agar (MEA) were 65 to 67 and 29 to 38 mm in diameter, respectively after incubation for 1 week at 25°C. Colonies on PDA and MEA were flat, slightly undulate, greenish gray in the center, greyish green at the margin; reverse black. Both isolates produced asexual structures. Pycnidia were black, granular, and grouped. Conidiogenous cells were hyaline, subcylindrical to cylindrical, 8.5 to 17.5 × 3 to 5.5 µm. Conidia were single-celled, hyaline, obovoid to ellipsoid, 5.2 to 9.4 × 3.6 to 7.5 µm (n = 50), smooth-walled, with a single apical appendage. Morphologically, both isolates resembled Phyllosticta capitalensis (Wikee et al. 2013). The internal transcribed spacer (ITS) region, large subunit (nrLSU), translation elongation factor 1-alpha (tef1-α), actin (act), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes were amplified using primer pairs ITS5/ITS4, LROR/LRO5, EF1-728F/EF2, ACT-512F/ACT-783R, and GPD1-LM/GPD2-LM, respectively (White et al. 1990; Zhang et al. 2022). Sequences were deposited in GenBank (ITS: PP946770, PP946771; nrLSU: PP948677, PP948678; tef1-α: PP948012, PP948013; act: PP948014, PP948015; GAPDH: PP948016, PP948017). Maximum likelihood phylogenetic analyses of the concatenated five genes identified both isolates as P. capitalensis. Thus, both morphology and molecular data confirmed the fungus as P. capitalensis. To confirm pathogenicity, healthy commercial guava fruits cultivar Kim Ju were surface disinfected by 0.1% NaClO for 3 min, rinsed three times with sterile distilled water, and wounded (Cruz-Lagunas et al. 2023). Conidia were collected from 2-week-old cultures on PDA and suspended in sterile distilled water. Fifteen microliters of a 1 × 106 conidia/ml suspension were dropped onto the wounded fruits. Mock inoculations were used as a control with sterile distilled water. Ten replications were conducted for each treatment and repeated twice. The inoculated fruits were stored in individual sterile plastic boxes at 25°C with 80 to 90% relative humidity. After 7 days, all inoculated fruits exhibited brown to dark brown lesions, while control fruits were asymptomatic. Phyllosticta capitalensis was consistently reisolated from the inoculated tissues on PDA to complete Koch's postulates. Prior to this study, P. capitalensis was known to cause brown or black spot disease on guava fruits cultivated in fields in China (Liao et al. 2020), Egypt (Arafat 2018), and Mexico (Cruz-Lagunas et al. 2023). To our knowledge, this is the first report of P. capitalensis causing postharvest brown spot disease on guava fruit in Thailand. The results will inform epidemiological investigations and future approaches to managing this disease.

First Report of Colletotrichum siamense Causing Leaf Anthracnose on Jackfruit in Thailand.

Jackfruit (Artocarpus heterophyllus Lam.) is commonly grown in Thailand. In June 2023, leaf anthracnose on this plant was observed at a field in Chai Prakan District (19°42'24"N, 99°01'59"E), Chiang Mai Province, Thailand, with ~25% disease incidence in a 1000-m2 plantation area. The initial symptom had brown spots with a yellow halo, enlarged, elongated, 0.2 to 2 cm in diameter, irregular, sunken, brown, with a dark brown halo, and leaves withered and dried. Pale yellow conidiomata developed on the lesions in high humidity. Ten symptomatic leaves were used to isolate the fungal causal agents through a single spore isolation method (Tovar-Pedraza et al. 2020). Four fungal isolates (SDBR-CMU492 to SDBR-CMU495) with similar morphology were obtained. Colonies on potato dextrose agar (PDA) were 70 to 85 mm in diameter, white to grayish white with cottony mycelia, the reverse pale yellow after incubation at 25°C for 1 week. All isolates produced asexual structures. Setae were brown with 1 to 3 septa, 40 to 100 × 2.2 to 4.0 µm, a cylindrical base, and acuminate tip. Conidiophores were hyaline to pale brown, septate, and branched. Conidiogenous cells were hyaline to pale brown, cylindrical to ampulliform, 7.4 to 27.2 × 2.0 to 4.5 µm. Conidia were one celled, hyaline, smooth walled, cylindrical, ends rounded, guttulate, 11.1 to 15.7 × 3.4 to 6.1 µm. Appressoria were dark brown to black, oval to irregular, 8.8 to 24.9 × 3.6 to 10 µm. Morphologically, all isolates resembled the Colletotrichum gloeosporioides species complex (Weir et al. 2012). The internal transcribed spacer (ITS) region, actin (act), ß-tubulin (tub2), calmodulin (CAL), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes were amplified using primer pairs ITS5/ITS4, ACT-512F/ACT-783R, T1/T22, CL1C/CL2C, and GDF1/GDR1, respectively (White et al. 1990; Weir et al. 2012). Sequences were deposited in GenBank (ITS: PP068858, PP068859, PP446789, PP446790; act: PP079636, PP079637, PP460760, PP460761; tub2: PP079638, PP079639, PP460762, PP460763; CAL: PP079634, PP079635, PP460758, PP460759; GAPDH: PP079632, PP079633, PP460756, PP460757). Maximum likelihood phylogenetic analyses of the concatenated five genes identified all isolates as C. siamense. To pathogenicity test, the mature leaves of a healthy plant were surface disinfested using 0.1% NaClO for 3 min, rinsed three times with sterile water, and wounded. Conidia suspensions (15 µl of 1 × 106 conidia/ml) of each isolate grown on PDA at 25°C for 2 weeks were used to inoculate wounded and unwounded samples by the attached method. Control leaves were mock inoculated with sterile distilled water. Ten replications were conducted for each treatment and repeated twice. Plants were placed in a greenhouse at 25 to 30°C and 80 to 90% relative humidity. After 7 days, all inoculated leaves displayed brown lesions, while control leaves had no symptoms. Colletotrichum siamense was reisolated from inoculated tissues on PDA to complete Koch's postulates. Prior to this study, C. fructicola and C. gloeosporioides caused leaf anthracnose on jackfruit worldwide (Sangchote et al. 2003; Chitambar 2016). Leaf anthracnose on jackfruit caused by C. siamense has been reported from Australia (James et al. 2014) and Bazil (Borges et al. 2023). In Thailand, Bhunjun et al. (2019) reported that C. artocarpicola causes leaf anthracnose in jackfruit. Therefore, this is first report of C. siamense causing leaf anthracnose on jackfruit in Thailand. The finding will inform epidemiological investigations and future approaches to managing this disease.