The Mi-1.2 gene confers resistance to a wide range of Meloidogyne species, being the most important resistance factor employed in tomato breeding so far. However, many aspects related to the interaction of Mi-1.2-carrying tomato cultivars and virulent/avirulent Meloidogyne populations have not yet been clarified. Herein, comparative histopathological analyses were carried after inoculation of the homozygous (Mi-1.2/Mi-1.2) tomato rootstock \'Guardião\' and the susceptible cultivar \'Santa Clara\' (mi-1.2/mi-1.2) with virulent and avirulent populations of M. javanica. In the susceptible control, it was possible to visualize second stage juveniles (J2) of avirulent population and feeding sites from 2 to 30 days after infection (DAI) with females reaching maturity at 24-34 DAI. In the resistant rootstock, the Mi-1.2 gene-mediated resistance was related mainly to early defense responses (pre-infection and hypersensitive reaction), which led to an immunity-like phenotype that completely prevented the reproduction of the avirulent Meloidogyne population. On the other hand, J2s of the virulent M. javanica population were able to penetrate roots much more than the avirulent population, migrated and developed normally, showing intense and similar pattern of penetration from 4 to 34 DAI in the root tissues of both resistant and susceptible tomato genotypes. The total numbers of J2, J3, J4, and females counted in \'Santa Clara\' for the virulent population of M. javanica were higher than in \'Guardião\'.

BACKGROUND: Abamectin (ABA) is considered a powerful insecticidal and anthelmintic agent. It is an intracellular product of Streptomyces avermitilis; is synthesized through complicated pathways and can then be extracted from mycelial by methanol extraction. ABA serves as a biological control substance against the root-knot nematode Meloidogyne incognita. This investigation is intended to reach a new strain of S. avermitilis capable of producing ABA effectively.
RESULTS: Among the sixty actinobacterial isolates, Streptomyces St.53 isolate was chosen for its superior nematicidal effectiveness. The mycelial-methanol extract of isolate St.53 exhibited a maximum in vitro mortality of 100% in one day. In the greenhouse experiment, the mycelial-methanol extract demonstrated, for the second-stage juveniles (J2s), 75.69% nematode reduction and 0.84 reproduction rate (Rr) while for the second-stage juveniles (J2s), the culture suspension demonstrated 75.38% nematode reduction and 0.80 reproduction rate (Rr). Molecular identification for St.53 was performed using 16 S rRNA gene analysis and recorded in NCBI Genbank as S. avermitilis MICNEMA2022 with accession number (OP108264.1). LC-MS was utilized to detect and identify abamectin in extracts while HPLC analysis was carried out for quantitative determination. Both abamectin B1a and abamectin B1b were produced and detected at retention times of 4.572 and 3.890 min respectively.
CONCLUSIONS: Streptomyces avermitilis MICNEMA2022 proved to be an effective source for producing abamectin as a biorational agent for integrated nematode management.

Root-knot nematode (Meloidogyne incognita) causes severe crop damage and large economic losses worldwide. Several cultivars of sweetpotato [Ipomoea batatas (L.) Lam)] have been developed with root-knot nematode-resistant traits; however, many of these cultivars do not have favorable agronomic characteristics. To understand the genetic traits underlying M. incognita resistance in sweetpotato, whole genome resequencing was conducted on three RKN-susceptible (Dahomi, Shinhwangmi, and Yulmi) and three RKN-resistant (Danjami, Pungwonmi, and Juhwangmi) sweetpotato cultivars. Three SNPs (single nucleotide polymorphisms) in promotor sequences were shared in RKN-resistant cultivars and were correlated with disease resistance. One of these SNPs was located in G6617|TU10904, which encoded a homolog of RIBOSOMAL PROTEIN EL15Z, and was associated with reduced expression in RKN-resistant cultivars only. Alongside SNP analysis, mRNA-seq data were analyzed for the same cultivars with and without nematode infection, and 18 nematode-sensitive genes were identified that responded in a cultivar-specific manner. Of these genes, expression of G8735|TU14367 was lower in sensitive cultivars than in RKN-resistant cultivars. Overall, this study identified two genes that potentially have key roles in the regulation of nematode resistance and will be useful targets for nematode resistance breeding programs.

Mexico is the 8th largest producer of tomatoes. Meloidogyne enterolobii is reported in Sinaloa, affecting tomato cultivars with genetic resistance to Meloidogyne spp. We aimed to evaluate field applications of fluopyram, fluensulfone, and fluazaindolizine treatments for managing M. enterolobii on tomatoes. Experiments were set on raised beds in a shade house. Nematicides were applied via drip irrigation. Under fluopyram treatment, M. enterolobii did not reduce the number of extra-large-size fruits. The number of large-size fruits with fluopyram and fluazaindolizine plus fluopyram treatments was also unaffected by M. enterolobii. Yield from the treatments fluopyram, fluazaindolizine plus fluopyram, and fluensulfone plus fluopyram was similar to the control treatment without M. enterolobii. Finally, fluazaindolizine plus fluopyram, fluopyram, and fluensulfone plus fluopyram treatments showed the highest reduction of root galling. We conclude that the fluopyram was more effective as an individual treatment. Pre-plant applications of fluensulfone and fluazaindolizine reduced the damage to the plant and the loss of yield; however, the complementary application of fluorinated nematicides improved the management of M. enterolobii in the tomato crop.

Research interest in the mechanisms enabling plant-parasitic nematodes to adjust their physiological performance and cope with changing temperatures has intensified in light of global warming. Here, we show that geographically distinct populations of the root-knot nematode Meloidogyne incognita, which is prevalent in the three main pepper-growing regions in Israel-Carmel Valley (Carmel), Jordan Valley (JV), and Arava Rift (Arava)-possess persistent differences in their thermal acclimation capacity, which affect pre- and postembryonic development. The optimal temperature for embryonic growth completion was 25°C for the Carmel population; 25 and 30°C for the JV population; and 30°C for the Arava population. Cumulative hatching percentages showed variations among populations; relative to hatching at 25°C, the Carmel population experienced hatching reduction at the higher studied temperatures 30 and 33°C, while the JV and Arava populations exhibited an increase in hatching at 30 and 33°C, respectively. Juvenile survival indicates that at the lowest temperature (20°C), the Carmel population gained the highest survival rates throughout the experimental duration, while at the same duration at 33°C, the Arava population gained the highest survival rate. Infective juveniles of the Carmel population demonstrated increased penetration of tomato roots at 25°C compared to the JV and Arava populations. Inversely, at 33°C, increased penetration was observed for the Arava compared to the Carmel and JV populations. Altogether, the Arava population\'s performance at 33°C might incur distinct fitness costs, resulting in consistent attenuation compared to the Carmel population at 25°C. Precisely defining a population\'s thermal acclimation response might provide essential information for models that predict the impact of future climate change on these populations.

The inhabitation and parasitism of root-knot nematodes (RKNs) can be difficult to control, as its symptoms can be easily confused with other plant diseases; hence, identifying and controlling the occurrence of RKNs in plants remains an ongoing challenge. Moreover, there are only a few biological agents for controlling these harmful nematodes. In this study, Xenorhabdus sp. SCG isolated from entomopathogenic nematodes of genus Steinernema was evaluated for nematicidal effects under in vitro and greenhouse conditions. The cell-free filtrates of strain SCG showed nematicidal activity against Meloidogyne species J2s, with mortalities of > 88% at a final concentration of 10%, as well as significant nematicidal activity against the three other genera of plant-parasitic nematodes in a dose-dependent manner. Thymine was isolated as active compounds by assay-guided fractionation and showed high nematicidal activity against M. incognita. Greenhouse experiments suggested that cell-free filtrates of strain SCG efficiently controlled the nematode population in M. incognita-infested tomatoes (Solanum lycopersicum L., cv. Rutgers). In addition, a significant increase in host plant growth was observed after 45 days of treatment. To our knowledge, this is the first to demonstrate the nematicidal activity spectrum of isolated Xenorhabdus species and their application to S. lycopersicum L., cv. Rutgers under greenhouse conditions. Xenorhabdus sp. SCG could be a promising biological nematicidal agent with plant growth-enhancing properties.

Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there\'s a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author\'s GitHub repository (https://github.com/bresilla/nematode_counting). While this study currently focuses on one genus, there are plans to expand the GUI\'s capabilities to include other economically significant genera of plant parasitic nematodes. Achieving these objectives, including enhancing the models\' accuracy on different imaging systems, may necessitate collaboration among multiple nematology teams and laboratories, rather than being the work of a single entity. With the increasing interest among nematologists in harnessing machine learning, the authors are confident in the potential development of a universal automated nematode counting system accessible to all. This paper aims to serve as a framework and catalyst for initiating global collaboration toward this important goal.

Siegesbeckia orientalis L., belonging to the family of Asteraceae and also known as \'Xi-Xian Cao\' or Herba Siegesbeckiae, has been an important traditional Chinese medicine since the Tang Dynasty (Wang et al., 2021). As the dried aerial parts have medicinal values, S. orientalis is widely grown in China, Japan, Korea, and Vietnam. One almost 600 m2 block of S. orientalis plants with stunting and leaf withering symptoms was found in Luonan County (110.26 E, 34.06 N), Shaanxi Province, in August 2022. Many galls were observed on the roots of these plants, and densities of second-stage juveniles (J2s) were 260~370 per 100 cm3 of soil. Females and eggs were dissected from infected roots, and J2s and males were extracted from the soil for species identification. The perineal patterns of females (n=20) were oval-shaped, with minor dorsal arches, distinct lateral fields, and tiny punctations around anus. The head caps of males were high and obviously narrower than head region which broadened out of the first body annuli. Morphological measurements of females (n=20) were: body length (L) = 897.66 ± 50.89 (860.96-949.74) μm, body width (BW) = 577.69 ± 51.01 (489.91-638.65) μm, stylet length (ST) = 14.03 ± 0.63 (13.25-14.97) μm, dorsal pharyngeal gland orifice to stylet base (DGO) = 4.96 ± 0.47 (4.08-5.37) μm, vulval slit length = 18.82 ± 1.97 (17.24-22.02) μm, vulval slit to anus distance = 13.62 ± 1.22 (12.34-16.18) μm. Measurements of males (n=10) were: L = 1298.73 ± 95.96 (1202.77-1394.69) μm, BW = 28.24 ± 2.38 (25.93-30.55) μm, ST = 20.23 ± 0.78 (19.42-21.04) μm, DGO = 4.89 ± 0.44 (4.56-5.22) μm, spicule length = 28.98 ± 1.68 (26.94-31.02) μm. Measurements of J2s: L = 375.35 ± 14.02 (341.01-400.46) μm, BW = 15.09 ± 1.47 (12.02-16.82) μm, ST = 12.74 ± 0.61(11.46-13.84) μm, DGO = 2.58 ± 0.59 (1.61-3.7) μm, tail length= 74.15 ± 13.73 (50.92-95.09) μm, hyaline tail terminus= 11.36 ± 2.27 (9.53-17.85) μm. These morphological characteristics were consistent with those of Meloidogyne hapla Chitwood, 1949 as described by Whitehead (1968). The DNA of single females (n=10) was isolated using the Proteinase K method for molecular identification (Kumari and Subbotin, 2012). The sequence of rDNA-ITS region was amplified and sequenced with the primers rDNA-F/R (TTGATTACGTCCCTGCCCTTT/TTTCACTCGCCGTTACTAAGG) (Vrain et al., 1992). The 768 bp sequence (GenBank OP542552) was 99.74% identical to the rDNA-ITS sequences of M. hapla (JX024147 and OQ269692). Then the D2/D3 fragments of the 28S rRNA were amplified and sequenced with the primers D2A/D3B (ACAAGTACCGTGAGGGAAAGTTG/TCGGAAGGAACCAGCTACTA) (McClure et al., 2012). The 762 bp fragment (OP554218) showed 100% identical to sequences of M. hapla (MN752204 and OM744204). To confirm the pathogenicity of the population, six 2-week-old healthy S. orientalis seedlings cultured in sterilized sand were each inoculated with 2,000 J2s hatched from egg masses. Four non-inoculated seedlings served as negative controls. After maintenance at 25°C for 60 days, galls appeared on the roots of inoculated plants, being consistent with the symptoms observed in field, while the negative controls showed no symptoms. Females collected from inoculated plants were identified as M. hapla with species-specific primer JWV1/ JWV (Adam et al., 2007), which amplified a fragment of 440 bp. Parasitism was also confirmed by the average recovery of 3,814 J2s per inoculated plant with the reproductive factor of 1.91. This is the first report of S. orientalis being a host of M. hapla. The disease reduces the quality and yield of S. orientalis, and much more efforts would be made for its control in production.

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Meloidogyne spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs (M. enterolobii R2 = 0.899, M. incognita R2 = 0.927, M. javanica R2 = 0.886), whereas a second contour-based approach used image analysis to identify eggs from their morphological characteristics and did not rely on neural networks (M. enterolobii R2 = 0.977, M. incognita R2 = 0.990, M. javanica R2 = 0.924). A third hybrid model combined these approaches and was able to detect and count eggs nearly as well as human raters (M. enterolobii R2 = 0.985, M. incognita R2 = 0.992, M. javanica R2 = 0.983). These automated counting protocols have the potential to provide significant time and resource savings annually for breeders and nematologists and may be broadly applicable to other nematode species.