Wilt Disease Research Articles

Evaluation of host plant species for a trap cropping strategy in integrated pest management of pineapple mealybug (Dysmicoccus brevipes)

Pineapple mealybugs are responsible for the emergence of pineapple wilt disease and the subsequent transmission of the pineapple disease virus. This study aimed to evaluate the alternate host plants as potential trap crops for pineapple mealybug. A total of nine host plant species, banana, pumpkin, roselle, hibiscus, ground fig, okra, java grass, goose grass and rhodes grass were selected for the study. Thirty adult females of pineapple mealybug were reared on a pumpkin for two months. Three replicates of a plot (area covered with black netting measuring 3 m × 3 m) were set up. Nine host plant species, approximately 2–3 feet in a 16 inch × 16 inch polybag were arranged in a circle around the pumpkin. The effectiveness of the host plant species was determined by calculating the infestation of each plant species for six months (with two-week observations). Our findings revealed that the pineapple mealybug prefers banana with the greatest mean and SD value (621.7±172.7), followed by pumpkin (237.0±45.6) and the lowest one is rhodes grass (2.00±26.5). ANOVA results show that there is a significant difference between host plant species (F-value of 26.87 and a P-value < 0.00). The observations suggest that banana has the potential to be utilized as trap crops for pineapple mealybug, and can be considered in IPM strategy to control pineapple mealybug.

SALINITY, VERTICILLIUM WILT TOLERANCE AND GENETIC DIVERSITY ANALYSIS OF UPLAND COTTON GENOTYPES

Cotton is one of the most economically important crops. Screening and selection salt-tolerant cotton genotypes using DNA molecular markers not only introduces tolerant cultivars valuable for hybridization and breeding programs, but also identifies DNA regions implicated in the salinity tolerance mechanism. Screening and selection of cotton genotypes resistant to salinity stress and Verticillium wilt disease using DNA molecular markers not only introduces tolerant cultivars valuable for hybridization and breeding programs, but also identifies DNA regions implicated in the tolerance mechanism. The lowest Disease Severity Index was recorded in AP-317 (0.37) and Tashkent-2 (0.32) varieties. A total of 63 bands were generated based on 12 ISSR primers, 50 of which were polymorphic. The highest PIC value was recorded for UBC841 (0.466). AP-317 of Azerbaijan origin and Kirqizistan-174 of Kyrgyzstan origin were the most distant (0.358) genotypes and at the same time showed high resistance to Verticillium wilt. The obtained results showed that ISSR markers were an effective tool for determining genetic variation and identification of cotton cultivars.

The synergistic effect of biosynthesized CuONPs and phage (ϕPB2) as a novel approach for controlling Ralstonia solanacearum

BackgroundAs a vital soil-borne pathogenic bacterium, Ralstonia solanacearum can cause wilt disease in multiple Solanaceae plants. Several phages, such as ϕPB2, could infect R. solanacearum acting as a potential biological control agent in soil. In addition, some nanoparticles, especially copper preparation, also showed high toxicity on R. solanacearum with low toxicity on plant. However, whether they can be administered in combination and how effective they are in inhibiting the plant disease caused by R. solanacearum is known very little.ResultsIn this work, the characterization of CuONPs using scanning electron microscope, transmission electron microscope, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction ascertained the presence of CuONPs which were nanometer particle of 83 nm. Then it was found that combined application of CuONPs with phage (ϕPB2) was superior to that of ϕPB2 or CuONPs alone in controlling tobacco bacterial wilt, with the CuONPs (250 mg/mL) and phage (106 PFU/mL) ratio being the best, at 79.1%. The combination of CuONPs and ϕPB2 also showed no obvious toxicity on tobacco growth than control like single application of CuONPs or ϕPB2. Furthermore, the transcriptome changes of R. solanacearum analysis indicated that the combination application and single allocation of CuONPs could inhibit “biofilm formation”, molecular function, biological processes, cellular components, metabolic process, and so on. In addition, the combination application showed higher inhibition of motility and biofilm, and better enhancement of cell membrane permeability, protein leakage, MDA concentration, and enzyme activity of their respiratory chain dehydrogenase than single application of CuONPs or phage (ϕPB2). Transcriptomes analysis also supported that the addition of ϕPB2 enhanced the toxicity of CuONPs by influencing the ABC transporters and quorum sensing, metabolic processes, and cellular biosynthetic processes of R. solanacearum.ConclusionIn total, our work not only proposed a novel way to increase the bactericidal effect of nanomaterials by adding phage, but also discovered the influence, synergistic effects, and mechanisms, which is useful to design novel way to combat phytopathogenic bacteria in the complicated environment.Graphical abstract

The complete mitochondrial genome and phylogenetic analysis of Cyanopterus ninghais (Hymenoptera: Braconidae)

Cyanopterus ninghais is an important gregarious ectoparasitoid during the larval stage of Monochamus alternatus, a key vector for pine wilt disease in Asia. In this study, the complete mitochondrial genome of C. ninghais was sequenced and analyzed. The mitochondrial genome of C. ninghais is 15,386 bp in length, comprising 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and 2 ribosomal RNA genes (rRNAs). The nucleotide composition is 41.32% A, 8.29% G, 6.06% C, and 44.33% T. Phylogenetic trees of Braconidae were constructed using 13 PCG sequences via Bayesian inference (BI) and maximum likelihood (ML) analyses to determine their phylogenetic position. Both ML and BI analyses revealed that C. ninghais is closely related to Euurobracon yokahamae, Virgulibracon endoxylaphagus, and Habrobracon hebetor.

Plant-induced bacterial gene silencing: a novel control method for bacterial wilt disease.

Ralstonia pseudosolanacearum, a notorious phytopathogen, is responsible for causing bacterial wilt, leading to significant economic losses globally in many crops within the Solanaceae family. Despite various cultural and chemical control strategies, managing bacterial wilt remains a substantial challenge. This study demonstrates, for the first time, the effective use of plant-induced bacterial gene silencing against R. pseudosolanacearum, facilitated by Tobacco rattle virus-mediated gene silencing, to control bacterial wilt symptoms in Nicotiana benthamiana. The methodology described in this study could be utilized to identify novel phytobacterial virulence factors through both forward and reverse genetic approaches. To validate plant-induced gene silencing, small RNA fractions extracted from plant exudates were employed to silence bacterial gene expression, as indicated by the reduction in the expression of GFP and virulence genes in R. pseudosolanacearum. Furthermore, treatment of human and plant pathogenic Gram-negative and Gram-positive bacteria with plant-generated small RNAs resulted in the silencing of target genes within 48 hours. Taken together, the results suggest that this technology could be applied under field conditions, offering precise, gene-based control of target bacterial pathogens while preserving the indigenous microbiota.

First report of bacterial wilt caused by Clavibacter michiganensis subsp. michiganensis affecting tomato in Iğdır

Tomato wilt disease caused by Clavibacter mihiganensis subsp. michiganensis (Cmm) is one of the most destructive tomato diseases and causes significant crop loss in both greenhouse and field tomato production areas worldwide. In this study, the presence of the causal agent of bacterial wilt disease in tomato plants was investigated in Aras Valley. Isolation was made from diseased plant samples and it was determined whether the strains were pathogenic by cellulase activity and HR test. The virulence, morphological and biochemical characteristics of the strains were determined. Strains that fatty acid methyl ester extraction, isolation and purification were performed were identified at species and subspecies level with % similarity index using gas chromatography system. The diagnosis was confirmed with the Biolog Gen III System and all strains were identified at the subspecies level with a % similarity index. As a result of this study, 57 strains were obtained in the isolation, and 39 of the strains were determined not to be pathogenic. Strain 18 was determined as the pathogen causing the most damage to tomato plants with 100 % disease severity. Strains were identified as Cmm at subspecies level with a similarity index of 71-87 % using gas chromatography system and 54-75 % similarity index with Biolog Gen III System. According to the heat map created, it was determined that the strains consisted of two main clusters. The presence of pathogen in Aras Valley was proved for the first time by this study.

Occurrence and potential diffusion of pine wilt disease mediated by insect vectors in China under climate change.

Pine wilt disease (PWD), a major international quarantined forest pest, causes serious ecological and economic damage to Pinus species in Asia and Europe. In China, PWD has spread northeasterly and northwesterly beyond its original northern limits. Consequently, an evaluation of the insect vector-mediated occurrence and potential diffusion of PWD is needed to identify important transmission routes and control the spread of disease. An optimized MaxEnt model was used to assess the current and future geographical distribution of Bursaphelenchus xylophilus and its insect vectors in China. The predicted suitable area for B. xylophilus colonization is currently 212.32 × 104 km2 and mainly concentrated in Central, East, Southwest and South China, although is anticipated to include the northwestern regions of China in the future. As for the insect vectors, Monochamus alternatus and M. saltuarius are expected to spread toward the northwest and southwest, respectively. The maximum predicted dispersion area of PWD mediated by M. alternatus, M. saltuarius and both species was 91.85 × 104, 218.76 × 104 and 29.99 × 104 km2, respectively, with potential diffusion areas being anticipated to increase in the future. Both the suitable probabilities and areas of B. xylophilus and its insect vectors were found to vary substantially along the latitudinal gradient, with the latitudinal range of these species being predicted to expand in the future. This is the first study to investigate the potential diffusion areas of PWD mediated by insect vectors in China, and our finding will provide a vital theoretical reference and empirical basis for developing more effective management strategies for the control of PWD in China. © 2024 Society of Chemical Industry.

Current Status and Prospects of Pine Wilt Disease Management with Phytochemicals-A Review.

PWD (pine wilt disease) is a devastating forest disease caused by the Bursaphelenchus xylophilus, which is the major invasive species in Asian and European countries. To control this disease, fumigation, pesticide injection, and clear cutting of epidemic trees have been widely used. But these management strategies have many limitations in terms of the effectiveness and environmental impacts, especially for the overuse of chemical pesticides. Thus, PCs (phytochemicals), the various compounds extracted from plants, have drawn extensive attention owing to their special characteristics, including abundant sources, low toxicity, high efficacy, and easy degradation. This review provides an overview of the current status of using PCs as alternative approaches to manage PWD. It discusses the efficacy of various PCs, the factors influencing their nematicidal activity, and their mechanism of action against B. xylophilus. These results will reveal the application of PCs in combating these devastating diseases and the necessity for further research.

Understanding the dynamics of Meloidogyne incognita infestation in pomegranate orchards of Himachal Pradesh, India (year 2018, 2019 and 2021) and its management strategies

This study investigates the prevalence and dynamics of pomegranate wilt disease induced by Meloidogyne incognita across the Kullu, Mandi and Solan districts of Himachal Pradesh (India), revealed notable spatial and temporal variations in nematode populations and galling severity across the regions. The highest average nematode infestation of 9.25 % was observed at Nauni with highest counts of average second-stage juvenile (J2) larvae (449 larvae per 100 cc of soil) followed by Hurla (Kullu) with 7.42 % infestation. Correlation analysis reveals a strong positive relationship between larval population and galling severity suggesting a potential link between nematode levels and plant damage. Common disease symptoms were leaf size reduction, yellowing and gradual decline of pomegranate plants, often observed in patches within orchards. Microscopic identification revealed distinctive pear-shaped body of mature females while J2 larvae displayed vermiform shapes and the associated species of M. incognita was confirmed through examination of the perineal pattern. Pathogenicity test reveals initiation of leaf yellowing symptom after 45 days of inoculation of larval suspension and root galling was observed after 60 days onward followed by plant decline under greenhouse conditions. Results from pot and field experiments demonstrated the efficacy of Fluopyram and Fluensulfone in reducing nematode populations and galling severity. Treatment with drenching of Fluopyram at the rate of 2 ml/L reduced 98.56 % larvae under field and 99.00 % larvae/100 cc soil under pot conditions. Statistical analysis (paired t-test and MANOVA) confirms significant differences in galling severity and larval population before and after drenching. The study also underscores the importance of weed management in disease mitigation as several weed species (Chenopodium album and Solanum nigrum) were identified as potential reservoirs for M. incognita in infested pomegranate plant basin. This investigation contributes to the advancement of management practices for pomegranate cultivation that addresses both nematode and weed infestations ultimately enhancing crop resilience and productivity.

Bioproducts derived from Bacillus stercoris isolate B.PNR1 and Streptomyces sp. isolate S.PNR29 for enhanced plant growth and disease control in tomato

ABSTRACT This study aims to develop a wettable formulation bioproduct from antagonistic bacteria, Bacillus stercoris isolate B.PNR1, and Streptomyces sp. isolate S.PNR29, targeting Fusarium oxysporum f.sp. lycopersici (Fol), the cause of Fusarium wilt disease in tomatoes, while also promoting plant growth. The survivability of bacterial cells in the product was assessed over a 180-day period at 4°C and 28°C. The bioproducts demonstrated significant control of Fusarium wilt disease, with B. stercoris isolate B.PNR1 bioproducts exhibiting superior disease control percentages compared to Streptomyces sp. isolate S.PNR29 bioproducts and commercial alternatives. Additionally, the bioproducts positively influenced tomato seed germination and seedling growth, with Streptomyces sp. isolate S.PNR29 bioproducts showing better growth performance in terms of plant length, weight measurements, and seed germination. In conclusion, B. stercoris isolate B.PNR1 and Streptomyces sp. isolate S.PNR29 bioproducts emerge as promising biocontrol agents, showcasing sustained efficacy against Fol and significant positive impacts on plant growth, thereby highlighting their potential for sustainable agriculture practices.

Promising antagonistic effect of bimetallic silver-selenium nanoparticles against Ralstonia solanacearum-causing wilt disease in eggplant (Solanum melongena L.)

Ralstonia solanacearum wilt disease causes significant economic losses to a number of crops worldwide, including eggplant (Solanum melongena L.). In this article plant growth promotions such as silver (Ag) nanoparticles (NPs), selenium (Se) NPs, and bimetallic Ag-Se NPs were used to combat the Ralstonia solanacearum wilt (RSW) disease and encourage growth-promoting strategies and resistance mechanisms in Solanum melongena L. Results clarified that the tested nano inducers against R. solanacearum (RS) have beneficial antibacterial. Bimetallic Ag-Se NPs (100 μg/mL) was the best treatment in reducing disease index (DI) by 17.5 and increasing the protection by 78.1 %, then Ag NPs and Se NPs, where the DI was 30 and 32.5 and protection by 62.5 % and 59.4 %, respectively. The potential resistance of developing eggplant plants was assessed using morphological characteristics, photosynthetic pigments, soluble carbohydrates, soluble protein, antioxidant enzyme activity, and isozymes. There was an increase in vegetative growth, photosynthetic pigments, soluble carbohydrates, and proteins in infected plants treated with all tested inducers, especially, bimetallic Ag-Se NPs. The amount of phenol content and the activity of antioxidant enzymes (PPO and POD) across diseased and healthy plants were recorded after applying all investigated inducers. The commercial use of a combination of tailored bimetallic Ag-Se NPs as nutritional therapy against R. solanacearum may be advised.

Unraveling the interactions of Esteya vermicola, pinewood nematode, and pine hosts: Insights into population dynamics and molecular responses

Esteya vermicola has shown promise as an efficient biological control agent against pine wilt disease, a devastating disease in pine forests caused by the pinewood nematode (PWN, Bursaphelenchus xylophilus). However, the in vivo interactions among E. vermicola, PWN, and pine hosts are less understood, both at the population and molecular levels. In this study, we performed a series of bioassays to investigate E. vermicola colonization patterns in pine xylem and its population responses to PWN invasion in healthy and PWN-induced wilting trees. Our results demonstrated that although E. vermicola exhibits slow growth, its conidia germinate and grew along the pine tracheid, even producing lunate conidia capable of initiating PWN infections within the xylem. Interestingly, while fungal hyphae became undetectable in pine sapling xylem after inoculation, the E. vermicola population increased immediately in response to PWN invasion. Furthermore, we observed a “leap-frog” dispersal pattern of fungal colonization in PWN-induced wilting pines, facilitated by the migration of fungal-infected nematodes. Moreover, we explored the molecular mechanisms underlying fungal tolerance to pine defense systems using transcriptomic analysis. Comparative transcriptomics revealed that carbohydrate metabolism and abiotic stress-induced oxidoreductive activities are involved in the fungal tolerance to the pine defense compound β-pinene. This study enhances our understanding of how E. vermicola colonizes and persists within pine xylem, its molecular responses to plant defense compounds, and its population dynamics upon PWN invasion, validating its efficacy as a biocontrol agent against pine wilt disease.

Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning

AbstractStrawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (gs) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (gs and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.

Linking landscape habitats with prevalence of fusarium wilt disease of cashew crop in Tanzania

Epidemic of Cashew Fusarium wilt disease (CFWD) has been a continuous focal challenge in the cashew farming, in Tanzania. Limited to edaphic conditions as a major factor in its epidemic, the current study aimed to assess the habitat-disease relationship. Purposive surveys involving assessment of disease prevalence and habitat compositions were conducted across four landscapes of southeastern zone from 2019 to 2023. Findings revealed a widespread of CFWD across diversified landscapes possessing varying habitat characteristics, mainly cultivated land with mature cashew, brownish sand loamy soils, grassland or shrub vegetation, seasonal river streamlines and natural water wells. The highest disease incidence and severity were noted at Nachingwea/Masasi plain (99.28:88.34%) followed by Liwale inland plain (98.64:89.3%), Coastal zone (72.72:59.83%) and Tunduru dissected plain (62.13:54.54%). The habitat characteristics were strongly similar within the landscape (0.86-Jaccard index) except between villages of the coastal zone (0.71-Jaccard index). Across landscapes, Nachingwea/Masasi plains and the Coastal zone were strongly similar to Tunduru dissected plain (0.63—1.0-Jaccard index), but strongly dissimilar with the Liwale inland plain (0.67—0.70- Jaccard distance). Furthermore, the presence of greater than 0.5 suitability indices across landscapes were revealed, with Liwale inland plain having strongest suitability index of 0.743 followed by Coastal zone (0.681), Tunduru dissected plain (0.617) and Nachingwea/Masasi plain. Significantly, the habitats had an increase of 0.1 suitability index, and positively correlated with disease prevalence by triggering disease incidence of 13.9% and severity of 31.4%. The study for the first time revealed the presence of an association between disease prevalence and landscape habitat characteristics of southeastern, Tanzania; paving the way to inclusive thinking of habitat as one of the drivers in the prevalence of fusarium wilt disease of cashews. Further research on the genetic coevolution of Fusarium oxysporum across landscapes to strengthen disease risk management in the cashew industry is recommended.

Classification and Severity Level Assessment of Fusarium Wilt Disease in Chickpeas using Convolutional Neural Network

Background: The fusarium wilt disease of chickpea leaves is a common illness that leads to economic problems for farmers due decreased crop yield. Early disease detection and the implementation of suitable precautions can help to increase the yield of chickpeas. This study offers an improved method for Fusarium wilt disease prediction based on severity level using a convolutional neural learning algorithm. Methods: The Convolutional Neural Network (CNN) model is utilized in this work to identify leaf disease due to wilting. The dataset contains 4,339 images of chickpea leaves that were obtained from Kaggle. After preprocessing, the data is sent into the network model for training. The model shows acceptable classification and accuracy metrics. Result: Deep learning methods are very useful tools for tracking leaf diseases at their early stages and can help farmers with the use of controlling methods. The proposed work looks for changes in the shape and color of chickpea leaves in order to predict severe fusarium disease. Training and validation accuracies show a balanced trade-off by giving satisfactory outcomes. The model shows an overall accuracy of 74.79%. The confusion matrix and classification parameters increase the model’s performance.

Analytical analysis and bifurcation of pine wilt dynamical transmission with host vector and nonlinear incidence using sustainable fractional approach

To study the dynamical system, it is necessary to formulate a mathematical model to comprehend the dynamics of the diseases that are prevalent around the world by using fractional calculus. A mathematical model is developed with the hypothesis created by adding control and asymptomatic variables to observe the rate of change of pine wilt and the ABC operator is used to turn the model into a fractional ordered model for continuous monitoring. The Boundedness and uniqueness of the developed model are investigated for bounded findings by using Banach space, which are the key properties of such an epidemic model. A newly developed system is examined both qualitatively and quantitatively to determine its stable position, and the verification of flip bifurcation has been made for developed systems. Derived reproductive numbers using the next-generation technique as well as the sensitivity of each involved parameter are verified. The Atangana–Toufik scheme is employed to find the solution for the developed system using different fractional values, which are advanced tools for reliable bounded solutions. Simulations have been made to see the real behavior and effects of pine wilt disease with control and asymptomatic battels in the community. Also, identify the real situation of the spread as well as the control of pine wilt after employing control and asymptomatic battels due to treatment. Such a type of investigation will be useful in investigating the spread of disease as well as helpful in developing control strategies based on our justified outcomes.

Deleting an xylosidase-encoding gene VdxyL3 increases growth and pathogenicity of Verticillium dahlia.

Verticillium dahliae causes a devastating Verticillium wilt disease on hundreds of plant species worldwide, including cotton. Understanding the interaction mechanism between V. dahliae and its hosts is the prerequisite for developing effective strategies for disease prevention. Here, based on the previous observation of an xylosidase-encoding gene (VdxyL3) in V. dahliae being obviously up-regulated after sensing root exudates from a cotton variety susceptible to this pathogen, we investigated the function of VdxyL3 in the growth and pathogenesis of V. dahliae by generating its deletion-mutant strains (ΔVdxyL3). Deleting VdxyL3 led to increased colony expansion rate, conidial production, mycelial growth, carbon and nitrogen utilization capacities, and enhanced stress tolerance and pathogenicity of V. dahliae. VdxyL3 is a secretory protein; however, VdxyL3 failed to induce cell death in N. benthamiana based on transient expression experiment. Transcriptomic analysis identified 1300 genes differentially expressed (DEGs) between wild-type (Vd952) and ΔVdxyL3 during infection, including 348 DEGs encoding secretory proteins, among which contained 122 classical secreted proteins and 226 non-classical secreted proteins. It was notable that of the 122 classical secretory proteins, 50 were carbohydrate-active enzymes (CAZymes) and 58 were small cysteine rich proteins (SCRPs), which were required for the pathogenicity of V. dahliae. The RNA-seq data thus potentially connected the genes encoding these proteins to the pathogenesis of V. dahliae. This study provides an experimental basis for further studies on the interaction between V. dahliae and cotton and the pathogenic mechanism of the fungus.

Tomato bacterial wilt disease outbreaks are accompanied by an increase in soil antibiotic resistance

The presence of soil-borne disease obstacles and antibiotic resistance genes (ARGs) in soil leads to serious economic losses and health risks to humans. One area in need of attention is the evolution of ARGs as pathogenic soil gradually develops, which introduces uncertainty to the dynamic ability of conventional farming models to predict ARGs. Here, we investigated variations in tomato bacterial wilt disease accompanied by the resistome by metagenomic analysis in soils over 13 seasons of monoculture. The results showed that the abundance and diversity of ARGs and mobile genetic elements (MGEs) exhibited a significant and positive correlation with R. solanacearum. Furthermore, the binning approach indicated that fluoroquinolone (qepA), tetracycline (tetA), multidrug resistance genes (MDR, mdtA, acrB, mexB, mexE), and β-lactamases (ampC, blaGOB) carried by the pathogen itself were responsible for the increase in overall soil ARGs. The relationships between pathogens and related ARGs that might underlie the breakdown of soil ARGs were further studied in R. solanacearum invasion pot experiments. This study revealed the dynamics of soil ARGs as soil-borne diseases develop, indicating that these ecological trends can be anticipated. Overall, this study enhances our understanding of the factors driving ARGs in disease-causing soils.

Effects of maternal body mass, age and crowding on offspring diapause induction and life-history traits in Monochamus alternatus alternatus (Coleoptera: Cerambycidae)

ABSTRACT Monochamus alternatus alternatus is a vector of pine wilt disease and has facultative diapause at the final larval instar. To determine the maternal effect on diapause and life-history traits of offspring, two sorts of rearing experiments were implemented. Two experiments indicated that the body mass of females did not affect the induction of offspring diapause and the heritability of adult body mass did not differ from zero. In another experiment, adults that underwent diapause at three crowding levels of 1, 2 and 4 larvae/pine bolt were allowed to oviposit during their lifetime. Newly hatched larvae were inoculated singly on Pinus thunbergii bolts. Development time from larval inoculation to adult emergence and adult body mass of diapause-forgoing offspring were not affected by larval crowding level in the maternal generation, maternal age, bark surface area of the bolt (a proxy for amount of food available) or offspring sex. Body mass and head capsule width of larvae in diapause decreased with the maternal age but increased with the bark surface area. However, there was no effect of crowding level in the maternal generation. In conclusion, this study found no maternal effects on the induction of offspring diapause, although the influence of maternal photoperiodic and temperature conditions remains to be determined.

Electron beam induced mutation in Curcuma longa L. against bacterial wilt disease

Electron beam induced mutation in Curcuma longa L. against bacterial wilt disease