Phytophthora Infestans Research Articles

Potato E3 ubiquitin ligase StRFP1 positively regulates late blight resistance by degrading sugar transporters StSWEET10c and StSWEET11.

Potato (Solanum tuberosum) is the fourth largest food crop in the world. Late blight, caused by oomycete Phytophthora infestans, is the most devastating disease threatening potato production. Previous research has shown that StRFP1, a potato Arabidopsis Tóxicos en Levadura (ATL) family protein, positively regulates late blight resistance via its E3 ligase activity. However, the underlying mechanism is unknown. Here, we reveal that StRFP1 is associated with the plasma membrane (PM) and undergoes constitutive endocytic trafficking. Its PM localization is essential for inhibiting P. infestans colonization. Through in vivo and in vitro assays, we investigated that StRFP1 interacts with two sugar transporters StSWEET10c and StSWEET11 at the PM. Overexpression (OE) of StSWEET10c or StSWEET11 enhances P. infestans colonization. Both StSWEET10c and StSWEET11 exhibit sucrose transport ability in yeast, and OE of StSWEET10c leads to an increased sucrose content in the apoplastic fluid of potato leaves. StRFP1 ubiquitinates StSWEET10c and StSWEET11 to promote their degradation. We illustrate a novel mechanism by which a potato ATL protein enhances disease resistance by degrading susceptibility (S) factors, such as Sugars Will Eventually be Exported Transporters (SWEETs). This offers a potential strategy for improving disease resistance by utilizing host positive immune regulators to neutralize S factors.

Deep Learning Based Tomato Leaf Disease Detection for Smart Farming

Tomato cultivation has a major role in global agriculture, contributing to both food security and economic stability. Still, tomato plants are susceptive to various diseases, which can specially reduce yield and quality. Plant disease early detection and treatment depend more and more on automatic disease identification and categorization technologies. In this paper, we present a Deep Learning based approach for tomato leaf diseases classification using VGG16 Convolutional Neural Network architecture. The VGG16 model is well-known for its superior picture categorization ability, A data set is used to train the VGG16 that includes healthy tomato leaves and leaves that are contaminated with common diseases like Early Blight, Late Blight, Leaf Mold, and Bacterial Spot. The dataset used in training and assessment comprises a varied assortment of high-resolution images acquired from various sources, guaranteeing the resilience and adaptability of the suggested model. Our utilization of data augmentation methods aims to improve the model's capacity to address variations in leaf characteristics, such as alterations in lighting, background complexity, and leaf positioning. Through experimental findings, we have validated the efficacy of our approach in precisely categorizing tomato leaf diseases with remarkable recall rates and precision. Comparative evaluations against leading techniques highlight the superior classification performance and computational efficiency of the VGG16-based model.

Various tomato infection discrimination using spectroscopy

AbstractDiagnosing plant diseases is a difficult task, but it could be made easier with the use of advanced instrumentation and the latest machine learning techniques. This paper is a further development of a previous authors study by the authors, which has been extended to provide the classification method for tomato diseases and to indicate the spectral ranges of greatest importance for this process. As tomatoes are one of the most popular and consumed vegetables, and diseases of this crop even reduce yields by up to 80% every year, their detection is a vague topic. This manuscript describes research in which spectroscopy was used to develop methods for discriminating between selected tomato diseases. The following, frequently occurring diseases were investigated for this research: anthracnose, bacterial speck, early blight, late blight, and Septoria Leaf Spot. The study used a dataset consisting of 3877 measurements taken with the ASD FieldSpec 4 Hi-Res spectroradiometer in the 350–2500 nm range from 2019/09/10 to 2019/12/20. The highest classification efficiency ($$F_1$$ F 1 score) of 0.896 was obtained for the logistic regression based model which was evaluated on Septoria Leaf Spot disease records.

Impacts of a Commercially Available Horticultural Oil Biopesticide (EF-400) on the Tomato-Phytophthora infestans Pathosystem.

Biopesticide fungicides are naturally derived compounds that offer protection from plant diseases through various modes of action, including antimicrobial activity and upregulation of defense responses in host plants. These plant protectants provide a sustainable and safe alternative to conventional pesticides in integrated disease management programs and are especially salient in the management of high-risk and economically important diseases such as late blight of tomato and potato, for which host resistance options are limited. In this study, a commercially available biopesticide, EF400 comprised of clove (8.2%), rosemary (8.1%), and peppermint oils (6.7%) (Anjon AG, Corcoran, CA), was investigated for its effects on the Phytophthora infestans-tomato pathosystem. Specifically, we evaluated the impact of EF400 on P. infestans growth in culture, disease symptoms in planta, and activation of host defenses through monitoring transcript accumulation of defense-related genes. The application timing and use rate of EF400 were further investigated for managing tomato late blight. EF400 delayed the onset of tomato late blight symptoms, although not as effectively as the copper hydroxide fungicide Champ WG (Nufarm Americas Inc., Alsip, IL). Pathogen mycelial growth and sporangial quantity on late blight-susceptible tomato leaves were significantly reduced with EF400. The biopesticide also had an enhancing or suppressing effect on the transcript accumulation of three defense-related genes: Pin2, PR1a, and PR1b. Our work in exploring a commercially available horticultural oil biopesticide meaningfully contributed to the essential knowledge base for optimizing recommendations for the management of tomato late blight.

Genome-wide identification analysis in wild-type Solanum pinnatisectum reveals some genes defending against Phytophthora infestans

Solanum pinnatisectum exhibits strong resistance to late blight caused by Phytophthora infestans but only an incomplete genome assembly based on short Illumina reads has been published. In this study, we generated the first chromosome-level draft genome for the wild-type potato species S. pinnatisectum in China using Oxford Nanopore technology sequencing and Hi-C technology. The high-quality assembled genome size is 664 Mb with a scaffold N50 value of 49.17 Mb, of which 65.87% was occupied by repetitive sequences, and predominant long terminal repeats (42.51% of the entire genome). The genome of S. pinnatisectum was predicted to contain 34,245 genes, of which 99.34% were functionally annotated. Moreover, 303 NBS-coding disease resistance (R) genes were predicted in the S. pinnatisectum genome to investigate the potential mechanisms of resistance to late blight disease. The high-quality chromosome-level reference genome of S. pinnatisectum is expected to provide potential valuable resources for intensively and effectively investigating molecular breeding and genetic research in the future.

Genome-wide analysis of polyphenol oxidase gene family in Solanum tuberosum and functional identification of StuPPO9 in regulating tolerance to Phytophthora infestans

Polyphenol oxidase (PPO) is the key enzyme involved in the enzymatic browning of potato tubers. However, the understanding of its other functions, as well as the underlying molecular mechanisms, is still limited. StuPPO9 was predicted as an inducible gene in response to disease defense and cell rescue, as analyzed using the EST database. In this study, an identification and evolutionary analysis of PPOs in Solanum tuberosum was conducted. A total of 10 members of StuPPOs were identified and their gene structure, protein motifs, chromosomal location, and tissue distribution were further investigated. QRT-PCR analysis indicated that most PPOs were induced or inhibited under hormone treatments, and StuPPO9 showed the highest expression level when induced by Phytophthora infestans. Therefore, StuPPO9, the only one located on chromosome 2, was selected for further study. The StuPPO9 overexpressing Nicotiana benthamiana transgenic line effectively slowed down the growth of P. infestans and showed more tolerance compared to the wild type (WT). Moreover, the molecular markers NtHin1 and PR genes were significantly upregulated in the transgenic tobacco than in WT under P. infestans infection. This study will provide a basis and reference for future research on the roles of StuPPO9 in disease defense.

Understanding the temporal dynamics of invasive late blight populations in India for improved management practices.

The microbial oomycete pathogen, Phytophthora infestans causes severe epidemics of potato late blight in crops globally. Disease management benefits from an understanding of the diversity of pathogen populations. In this study, we explore the dynamics of P. infestans populations in the late blight-potato agro-ecosystem across the Indian subcontinent. Investigations of the macroecological observations at the field level and microbial ecological principles provided insights into future pathogen behaviour. We use a comprehensive simple sequence repeat allele dataset to demonstrate that an invasive clonal lineage called EU_13_A2 has dominated populations over 14 years across India, Bangladesh, and Pakistan. Increasing levels of sub-clonal variation were tracked over time and space and, for the first time, populations in Asia were also compared to the source populations from Europe. Within India, a regional pathogen population structure was observed with evidence for local migration, cross-border movement between surrounding countries, and introductions via imports. There was also evidence of genetic drift and between-season transmission of more strongly pathogenic sub-clones with a complete displacement of some sub-clonal types. The limited introduction of novel genotypes and the use of resistant potato cultivars could contribute to the dominance of the 13_A2 lineage. The insights will contribute to the management of the pathogen in these key global potato production regions.

An efficient deep learning model for tomato disease detection

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

Leaf and tuber resistance to <I>Phytophthora infestans</I> and relationship between these traits in wild potato species

Background. Phytophthora infestans (Mont.) de Bary is a well-known serious pathogen that affects a wide range of currently grown potato cultivars. Control of the disease, including the breeding process, is complicated by the fact that leaf resistance to P. infestans is often unrelated to tuber resistance, resulting in the need to identify individual plants with leaf and/or tuber resistance to be used in breeding programs. In view of this, research efforts to identify such sources are quite relevant. The objective was to assess the occurrence of plants with resistance in both organs among different wild species and analyze the relationship between leaf and tuber resistance within them.Material and methods. Assessment of leaf and tuber resistance in 97 accessions belonging to 36 species was carried out under artificial inoculation. Using the data obtained on each individual plant, the connection between leaf and tuber resistance was analyzed by means of the Wilcoxon matched-pairs test and Spearman’s rank-order correlations.Results. The species with more frequent occurrence of plants combining leaf and tuber resistance and the species with the predominance of leaf or tuber resistance were identified. The statistical analysis did not show complete (100%) correlations between leaf and tuber resistance in any of studied species. In some cases, a significant positive or negative correlation between these characters was found. The assessment results for the studied species/accessions can facilitate the search for leaf and/or tuber resistance sources capable of improving potato cultivars susceptible to late blight.

Early Blight and Late Blight Disease Detection in Potato Using Efficientnetb0

Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop during its production. A typical Indian farmer lacks the tools necessary to detect Leaf Disease before damage is done. On a dataset of potato leaf images retrieved from Kaggle, we employed the EfficientNetB0 of Deep Learning to address this problem. This model uses width scaling and resolution scaling apart from depth scaling to perform the classification. Our work mainly focuses on the diseases Early Blight and Late Blight, two serious potato diseases. Early blight Spots start off as tiny, dry, dark, and papery specks that develop into brown to black, circular to oval-shaped regions. Veins that round the spots frequently give them an angular appearance. Late blight syntoms appear as small, light to dark green and round to irregularly shaped. Water-soaked patches are the first signs of late blight. The Data Collection has 2152 pictures in total, 2000 of which are diseased and 152 of which are healthy. The deep learning model provides a testing accuracy of 99.05%, which is higher than several widely used techniques available to provide farmers with knowledge about correct diseases well in time.

Leveraging Inception V3 for Precise Early and Late Blight Disease Classification in Potato Crops

Leveraging Inception V3 for Precise Early and Late Blight Disease Classification in Potato Crops

Botrytis cinerea detoxifies the sesquiterpenoid phytoalexin rishitin through multiple metabolizing pathways

Botrytis cinerea is a necrotrophic pathogen that infects across a broad range of plant hosts, including high-impact crop species. Its generalist necrotrophic behavior stems from its ability to detoxify structurally diverse phytoalexins. The current study aims to provide evidence of the ability of B. cinerea to tolerate the sesquiterpenoid phytoalexin rishitin, which is produced by potato and tomato. While the growth of potato pathogens Phytophthora infestans (late blight) and Alternaria solani (early blight) was severely inhibited by rishitin, B. cinerea was tolerant to rishitin. After incubation of rishitin with the mycelia of B. cinerea, it was metabolized to at least six oxidized forms. Structural analysis of these purified rishitin metabolites revealed a variety of oxidative metabolism including hydroxylation at C7 or C12, ketone formation at C5, and dihydroxylation at the 10,11-olefin. Six rishitin metabolites showed reduced toxicity to P. infestans and A. solani, indicating that B. cinerea has at least 5 distinct enzymatic reactions to detoxify rishitin. Four host-specialized phytopathogenic Botrytis species, namely B. elliptica, B. allii, B. squamosa, and B. tulipae also had at least a partial ability to metabolize rishitin as B. cinerea, but their metabolic capacity was significantly weaker than that of B. cinerea. These results suggest that the ability of B. cinerea to rapidly metabolize rishitin through multiple detoxification mechanisms could be critical for its pathogenicity in potato and tomato.

The danger of the spread of Ralstonia solanacearum on potato crops worldwide and potential mitigation options

Abstract Potato is a major crop that is consumed by over one billion people worldwide. However, production is hampered by diseases such as viral infections, late blight, and bacterial wilt. Bacterial wilt disease caused by Ralstonia solanacearum is the second most prevalent disease after blight, and its effects are exacerbated by both global trade and climate change. The extent of the disease devastation varies by country, with more serious consequences evident in major potato-producing countries. The Ralstonia species complex (RSSC) is a group of pathogenic strains that spread through the use of infected seed potato, soils, water run-offs, plant debris, or contaminated farm equipment and propagation material. The elements that contribute to the rapid spread of bacterial wilt disease in potato are discussed in this review article, and a disease-control strategy is proposed. Understanding how R. solanacearum spreads through international trade is critical for developing effective mitigation and management techniques to control and limit the spread of this damaging phytopathogen.

Computational neural network for processing light-reflective spectra of plants and remote phytosanitary monitoring of potatoes

The article is devoted to studying the possibility of using the WaveLetNN artificial neural network to analyze the results of remote phytosanitary monitoring of early detection of plants in potato plantings affected by late blight. Various methods for analyzing the spectral characteristics of plant reflection are considered, including the classification method. To detect plants infected with late blight, the WaveLetNN neural network analyzes the light reflective characteristics of potato plants obtained as a result of research (in the range of 300–1100 nm) and calculates the cognitive significance index (CSI = 0...10), which characterizes the intensity of biochemical processes inside plants aimed at countering phytopathogenic microflora. It was found that a significant increase in the CSI index signals infection of plants by phytopathogenic microflora and activation of protective biochemical processes on the part of plants. To reliably indicate infected plants, the WaveLetNN neural network underwent test training on a large number of light reflectance spectra of uninfected plants and plants artificially infected with late blight. The spectral reflectance characteristics of infected and uninfected plants were measured during 3, 4, 7 and 8 days after infection. Processing the obtained spectra using the WaveLetNN neural network made it possible to identify significant differences between the second- and third-order spectral characteristics of uninfected and late blight infected plants on the third day after infection. Moreover, for infected plants the CSI index values were 6.1...6.7, and CSI for healthy plants – 1.9...2.5. The Wave-LetNN neural network eliminates the influence on the light reflectance spectra of the spatial arrangement of plant leaves, unevenness of the soil surface and shading of individual sections of the field, normalizing the spectra to the total intensity of light reflected from the leaves. Thus, the WaveLetNN neural network can be used as the software core of online systems for remote phytosanitary monitoring of potato plants.

Isolation and Characterization of Pathogenic Mold Causing Potato Tuber Rot Disease

The potato plant (Solanum tuberosum L.) is a perennial crop that contains high amounts of carbohydrates, minerals, and vitamins in its tubers, making it a carbohydrate-rich alternative food to rice or corn. One of the diseases that often appear on potato plants is potato rot caused by pathogenic molds. This study aims to isolate and characterize pathogenic fungi that cause blight on potato tubers based on Koch's Postulates. Potato pathogenic molds were isolated from potato tubers that had been rotted, then the molds were grown on PDA and purified twice. The purified isolates were then inoculated onto 30 healthy potato tubers and incubated for 7 days. Healthy potato tubers experienced the same symptoms as potato tuber rot. The isolated pathogenic fungi were then characterized so that the pathogenic fungi of Phytophthora infestans were obtained. In this study, Koch's Postulate method was successfully applied to isolate and characterize the pathogenic fungi that cause potato tuber rot. The pathogenic fungi caused a potato tuber rot disease incidence value of 100% with a disease severity value of 60.7%. The two factors that determine the disease incidence and disease severity values are internal factors (genes and traits of the pathogen) and external factors (environment).

Combined effects of fungicides formulations and potato varieties on late blight management, yield and net farm income in Kenya

AbstractManagement of late blight relies on the use of fungicides and potato varieties which have been associated with loss of efficacy over time. Baseline survey and field studies were conducted to evaluate the effects of fungicide formulations and potato varieties in managing late blight and subsequently on net farm income improvement. Four fungicides formulations (Infinito® (Fluopicolide 62.5g L−1 + propamocarb 625 g L−1), Milraz® (Propineb 700 g kg−1 + cymoxanil 60 g kg−1), Mistress 72® (Mancozeb 640 g kg−1 + cymoxanil 80 g kg−1) and Ridomil® (Metalaxyl 40 g kg−1 + mancozeb 640 g kg−1)), and varieties (Kenya Mpya, Shangi and Dutch Robijn) were evaluated. Survey results revealed that the majority (78%) of the farmers selected varieties to grow based on market dynamics as compared to seed availability (18%), disease resistance (12%) and other factors (8%). The most widely and least used fungicides were Ridomil® (28.8%) and Infinito® (1.7%) respectively. Late blight significantly reduced crop growth (height and stem count) and yield in the unprotected plots in Shangi and Dutch Robijn, but no effect was observed in K. Mpya. Mistress® suppressed blight symptoms by 54%, while Milraz® (lowest effects) reduced the disease by (43%). In unprotected plots, disease severity on K. Mpya was below 1%, while in Shangi and Dutch Robijn varieties, it was 60 and 78% respectively. Reduction in disease by Mistress® resulted in higher yield (30.70 t ha−1) as compared to the unprotected plots (9.25 t ha−1). Maximum yield (28.56 t ha−1) was obtained from K. Mpya, while Dutch Robijn, had the lowest observed yield (20.70 t ha−1) in protected plots. Yet, the highest cost benefit ratio (CBR) was recorded in unprotected plots with K. Mpya. The smallest CBR (1.55) was observed on plots where Milraz® was applied on Dutch Robjn. Results of this study show that, the use of fungicides reduced late blight and improved yield of potato substantially, but the efficacy varied with fungicide formulation and potato variety. However, the use of resistant variety was effective in managing late blight hence a useful tool for minimizing fungicide applications. Therefore, use of resistant variety could enhance a healthy biodiversity and at the same time maximize food production.

Opportunity of the NEGFRY Decision Support System for the Sustainable Control of Potato Late Blight in A Limia (NW of Spain)

The NEGFRY system is presented in this research work as a novelty strategy for the control of potato late blight in A Limia (NW Spain). The aim was to analyse the number of fungicide applications established by NEGFRY (Decision Support System, DSS) with respect to the routine calendars of this potato production area. This approach is in accordance with the requirements imposed by European policies, participating in more profitable and environmentally friendly agriculture. For this purpose, the relationships between the pathogen and the environmental conditions and epidemiological parameters were analysed in different plots compared to late blight infection in both Routine and DSS strategies. In addition, economic and environmental impacts of fungicide sprays were also analysed to check the adaptability of the decision support system to late blight in the area. Results confirmed that it is possible to reduce sprays following the NEGFRY model by more than 50% compared with Routine applications. Therefore, NEGFRY is a promising strategy to achieve lower footprint potato yields through a reduction in applied pesticides and water, lower gas emissions and less soil compaction due to a reduction in the number of tractor passes over the soil. The commitment to this strategy contributes to improving the economy of farmers, the health of the biodiversity, the environment and consumers, as well as to comply with demands of global economies in terms of more sustainable and resilient agriculture.

First Report of Alternaria linariae Causing Postharvest Fruit Rot of Pepino (Solanum muricatum) in China.

Pepino (Solanum muricatum L.) is a popular solanaceous crop that is native to South America and is commercially grown in many countries including China for its attractive, sweet and flavorful fruits. In September 2023, a postharvest fruit rot was observed at an incidence of 7% to 10% on pepino at supermarket in Nanchang, Jiangxi, China (28.69°N, 115.81°E). Symptoms on fruits initially appeared as small black spots that later enlarged and became necrotic. To isolate the pathogen, symptomatic tissues were surface-sterilized using 75% ethanol for 15 s, then 1% sodium hypochlorite for 30 s, rinsed three times in sterile water, air dried, finally placed on potato dextrose agar (PDA) plates and incubated at 25℃ for 4 days. Ten strains (about 83% isolationfrequencyfromsymptomaticpepinofruits) with similar morphological characteristics were isolated. The colonies on PDA were initially white, gradually turning gray and eventually becoming black, and had abundant aerial mycelia. Conidia were fusiform to linetype, dark brown, measuring 50 to 100 × 10 to 28 μm (n = 30) with 5 to 10 transverse septa and 0 to 3 longitudinal septa. Based on the morphological characteristics, the pathogen was identified as Alternaria sp. (Ma et al. 2021). To further confirm species, two representative isolates (JXAL-1 and JXAL-2) were selected for molecular identification. The internal transcribed spacer (ITS) region, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1-alpha (TEF1), Alternaria major allergen gene (Alt a 1) and DNA-directed RNA polymerase II core subunit (RPB2) were amplified and sequenced by using primers ITS5/ITS4, gpd1/gpd2, Alt-for/Alt-rev, EF1-728F/EF1-986R and fRPB2-5F2/fRPB2-7cR (Woudenberg et al. 2013; Woudenberg et al. 2014), respectively. These sequences were deposited into GenBank with accession number PP231808-PP231809 (ITS), PP238480-PP238481 (GAPDH), PP238482-PP238483 (Alt a 1), PP238484-PP238485 (TEF1) and PP238486-PP238487 (RPB2). A BLASTn homology search for these nucleotides showed 100% identity to ITS (KJ718182, 525 nt/525 nt), GAPDH (KJ718026, 579 nt/579 nt), Alt a 1 (KJ718694, 472 nt/472 nt), TEF1 (KJ718530, 334 nt/334 nt) and RPB2 (KJ718355, 772 nt/772 nt) sequences of Alternaria linariae CBS 107.61. The maximum likelihood analyses were performed for the combined ITS, GAPDH, Alt a 1, TEF1 and RPB2 using the IQtree web server (Trifinopoulos et al. 2016). In the phylogenetic tree, the isolates and isolates of A. linariae clustered together with 100% bootstrap support. Therefore, the fungus was identified as A. linariae. To evaluate pathogenicity, five healthy fruits of pepino were surface-sterilized with 75% ethanol, then wounded and a 5 mm diameter agar with isolate JXAL-1 was put on the wound. Another five fruits was inoculated with sterile agar plugs as control. All treated fruits were incubated at 25 ℃ with 80% humidity , and repeated twice. Five days later, all the wounded fruits inoculated with A. linariae showed similar symptoms and A. linariae was reisolated, while the control fruits remained healthy and no pathogen was isolated, fulfilling Koch's postulates. A. linariae is known as an important pathogen causing early blight of tomato and potato(Adhikari et al. 2020). To our knowledge, this is the first report of A. linariae causing postharvest fruit rot on S. muricatum in China, which expands the natural host range of A. linariae and will be helpful to develop efficient management strategies on pepino.

Salivary Protein Cyclin-Dependent Kinase-like from Grain Aphid Sitobion avenae Suppresses Wheat Defense Response and Enhances Aphid Adaptation.

Aphids are insect pests that suck phloem sap and introduce salivary proteins into plant tissues through saliva secretion. The effector of salivary proteins plays a key role in the modulation of host plant defense responses and enhancing aphid host adaptation. Based on previous transcriptome sequencing results, a candidate effector cyclin-dependent kinase-like (CDK) was identified from the grain aphid Sitobion avenae. In this study, the function of SaCDK in wheat defense response and the adaptation of S. avenae was investigated. Our results showed that the transient overexpression of SaCDK in tobacco Nicotiana benthamiana suppressed cell death triggered by mouse pro-apoptotic protein-BAX or Phytophthora infestans PAMP-INF1. SaCDK, delivered into wheat cells through a Pseudomonas fluorescens-mediated bacterial type III secretion system, suppressed callose deposition in wheat seedlings, and the overexpression of SaCDK in wheat significantly decreased the expression levels of salicylic acid and jasmonic acid signaling pathway-related genes phenylalanine ammonia lyase (PAL), pathogenesis-related 1 protein (PR1), lipoxygenase (LOX) and Ω-3 fatty acid desaturase (FAD). In addition, aphid bioassay results showed that the survival and fecundity of S. avenae were significantly increased while feeding on the wheat plants carrying SaCDK. Taken together, our findings demonstrate that the salivary protein SaCDK is involved in inhibiting host defense response and improving its host adaptation, which lays the foundation to uncover the mechanism of the interaction of cereal aphids and host plants.

De novo assembly-based transcriptome analysis of resistant and susceptible potato varieties to Phytophthora infestans

AbstractAn effective tool for discovering differentially expressed genes (DEGs) related to late blight (LB) resistance is the transcriptome sequencing of potatoes. The aim of this study was to compare transcriptome expression analysis in incompatible and compatible interactions via high-throughput sequencing. Furthermore, we performed a bioinformatics analysis to screen a large number of specific transcription factors (TFs) and DEGs linked to Phytophthora infestans infection. Two locally cultivated potato varieties were chosen from evaluation assays conducted in two consecutive seasons and based on the disease severity (DS) values. These varieties were the highly resistant Jelly (HR) to P. infestans and the moderately susceptible Annabelle (MS). Ribonucleic acid-sequencing (RNA-seq) was achieved for the two varieties with their controls through the BGISEQ-500 sequencing platform. The RNA-seq analysis identified P. infestans-responsive genes and their expression in potatoes. The mechanism of the response of these cultivars to the P. infestans pathogen by TFs and DEG genes, which play an important role in defense response, was investigated. The Gene Ontology (GO) analysis classified 46,248 unigenes in the HR and 26,921 unigenes in MS into the following three categories: biological process, cellular component, and molecular functions. More genes were responsible for the cellular component category, biological process, and molecular functions in HR compared to MS. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the significantly enriched DEGs were included in the plant–pathogen interaction, biosynthesis of secondary metabolites, and ribosome. In addition, 1874 transcription factor genes belonging to 85 families were indicated in the DEGs, of which MYB and AP2-EREBP genes were the most abundant. Besides, multiple genes related to LB resistance showed differential expression during infection. It also sheds light on the molecular mechanisms behind potato resistance to P. infestans infection.