Semi-Targeted Profiling of the Lipidome Changes Induced by Erysiphe Necator in Disease-Resistant and Vitis vinifera L. Varieties.

The ascomycete Erysiphe necator is a serious pathogen in viticulture. Despite the fact that some grapevine genotypes exhibit mono-locus or pyramided resistance to this fungus, the lipidomics basis of these genotypes' defense mechanisms remains unknown. Lipid molecules have critical functions in plant defenses, acting as structural barriers in the cell wall that limit pathogen access or as signaling molecules after stress responses that may regulate innate plant immunity. To unravel and better understand their involvement in plant defense, we used a novel approach of ultra-high performance liquid chromatography (UHPLC)-MS/MS to study how E. necator infection changes the lipid profile of genotypes with different sources of resistance, including BC4 (Run1), "Kishmish vatkhana" (Ren1), F26P92 (Ren3; Ren9), and "Teroldego" (a susceptible genotype), at 0, 24, and 48 hpi. The lipidome alterations were most visible at 24 hpi for BC4 and F26P92, and at 48 hpi for "Kishmish vatkhana". Among the most abundant lipids in grapevine leaves were the extra-plastidial lipids: glycerophosphocholine (PCs), glycerophosphoethanolamine (PEs) and the signaling lipids: glycerophosphates (Pas) and glycerophosphoinositols (PIs), followed by the plastid lipids: glycerophosphoglycerols (PGs), monogalactosyldiacylglycerols (MGDGs), and digalactosyldiacylglycerols (DGDGs) and, in lower amounts lyso-glycerophosphocholines (LPCs), lyso-glycerophosphoglycerols (LPGs), lyso-glycerophosphoinositols (LPIs), and lyso-glycerophosphoethanolamine (LPEs). Furthermore, the three resistant genotypes had the most prevalent down-accumulated lipid classes, while the susceptible genotype had the most prevalent up-accumulated lipid classes.

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM.

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200-3202 cm-1) was compared. Finally, five spectral preproccessing algorithms, Savitzky-Golay 1-Der (SGD), Savitzky-Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm's accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

Comprehensive polyphenolic profiling in promising resistant grapevine hybrids including 17 novel breeds in northern Italy.

BACKGROUND: A promising way to overcome the susceptibility of Vitis vinifera L. to fungal diseases is the integration of genetic resistance by the interspecific crossing between V. vinifera varieties and resistant species. However, the products of such hybrids are still not accepted by customers, particularly due to their organoleptic characteristics, not least influenced by their polyphenolic profile. RESULTS: A total of 58 resistant breeding lines, 41 from international programs and 17 new progeny individuals, were grown in one untreated vineyard to exclude any variances by climatic and pedologic conditions or vineyard practice. A total of 60 polyphenols (including acids, anthocyanins, flavonols, flavan-3-ols, and stilbenoids) were determined in grapevine berries by ultrahigh-performance liquid chromatography-mass spectrometry in two consecutive years. The overall profiles were rather consistent (variation P > 0.05) within the two harvests, with the exceptions of epicatechin and caftaric acid. Anthocyanin diglucosides were found in ten of the red breeding lines, malvidin-3,5-O-diglucoside being predominant in nine of them. Total polyphenol content of the unknown progeny individuals and international breeding lines was comparable, with the exception of significantly increased amounts of gallic acid and some flavonoids. CONCLUSION: The comprehensive study reported herein of the polyphenolic profile of hybrids from international breeding programs, but also of new breeds from private initiatives, all cultivated in the same vineyard, will support the selection of promising candidates for further breeding programs to overcome impairment due to undesired sensory characteristics of new highly resistant varieties.

Survey of Incidence and Nested PCR Detection of Sugarcane White Leaf in Different Varieties.

Sugarcane white leaf (SCWL) is a devastating sugarcane (Saccharum officinarum) disease caused by a 16SrXI group phytoplasma, which is extremely harmful to sugarcane production. To determine the occurrence of SCWL in different varieties in 2018, we conducted a field survey and performed nested PCR detection of SCWL phytoplasma in cane-planting areas of Mangweng and Hepai in Gengma, Yunnan province, which are the areas most severely affected by SCWL in China. The results of the field survey showed that the symptomatic incidence of SCWL differed among varieties. The mean symptomatic incidence of SCWL on variety Yuetang60 was the highest (73.50%), and it was the lowest on Liucheng05-136 (13.67%). Using nested PCR, the SCWL phytoplasma was detected in symptomatic plants of all varieties more than 90% of the time; the SCWL phytoplasma was detected in 91 and 97% of symptomatic plants of Yingyu91-59 and Liucheng05-136 varieties, respectively. The SCWL phytoplasma was detected by PCR in 82% of the asymptomatic plant samples. The results of this study showed that field survey based on white leaf symptoms did not accurately reflect the actual occurrence of the SCWL phytoplasma.

Impact of Climate Change on Phenology of Two Heat-Resistant Wheat Varieties and Future Adaptations.

Climate change (CC) is a global threat to the agricultural system. Changing climatic conditions are causing variations in temperature range, rainfall timing, humidity percentage, soil structure, and composition of gases in environment. All these factors have a great influence on the phenological events in plants' life cycle. Alternation in phenological events, especially in crops, leads to either lower yield or crop failure. In light of respective statement, the present study is designed to evaluate the climatic impacts on two heat-resistant wheat varieties (Sialkot-2008 and Punjab-2018). During the study, impacts of CC on wheat phenology and annual yield were predicted considering six climatic factors: maximum temp, minimum temperature, precipitation, humidity, soil moisture content, and solar radiation using two quantitative approaches. First, a two-year field experimental plot was set up at five different sites of study-each plot a bisect of two sites. Phenological changes of both varieties were monitored with respect to climatic factors and changes were recorded in a scientific manner. Secondly, experimental results were compared with Global climate models (GMC) models with a baseline range of the past 40 years (1970-2010) and future fifty years (2019-2068) under Representative Concentration Pathway (RCP) 8.5 model analysis. Field experiment showed a (0.02) difference in maximum temperature, (0.04) in minimum temperature, (0.17) in humidity, and about (0.03) significant difference in soil moisture content during 2019-2021. Under these changing climatic parameters, a 0.21% difference was accounted in annual yield. Furthermore, the results were supported by GMC model analysis, which was analyzed by Decision Support System for Agrotechnology Transfer (DSSAT) model. Results depicted that non-heat-resistant wheat varieties could cause up to a 6~13% reduction in yield during future 50 years (2019-2068)) compared with the last 40 years (1970-2010). A larger decline in wheat grain number relative to grain weight is a key reducer of wheat yield, under future climate change circumstances. Using heat-tolerant wheat varieties will not only assist to overcome this plethora but also provide a potential increase of up to 7% to 10% in indigenous environment. On the other hand, it was concluded that cultivating these heat-resistant varieties that are also ripening late culminates into enhanced thermal time chucks during the grain-filling period; hence, wheat yield will increase by 8% to 12%. In changing climatic conditions and varieties, 'Punjab-2018' will be the better choice for peasants and farm-land owners to obtain a better yield of wheat to cope with the necessities of food on the domestic and national level.

Potato Zebra Chip: An Overview of the Disease, Control Strategies, and Prospects.

Potato (Solanum tuberosum L.) is an important food crop worldwide. As the demand for fresh and processed potato products is increasing globally, there is a need to manage and control devastating diseases such as zebra chip (ZC). ZC disease causes major yield losses in many potato-growing regions and is associated with the fastidious, phloem-limited bacterium Candidatus Liberibacter solanacearum (CLso) that is vectored by the potato-tomato psyllid (Bactericera cockerelli Sulc). Current management measures for ZC disease mainly focus on chemical control and integrated pest management strategies of the psyllid vector to limit the spread of CLso, however, they add to the costs of potato production. Identification and deployment of CLso and/or the psyllid resistant cultivars, in combination with integrated pest management, may provide a sustainable long-term strategy to control ZC. In this review, we provide a brief overview of the ZC disease, epidemiology, current management strategies, and potential new approaches to manage ZC disease in the future.

Comparative Programs for Arthropod, Disease and Weed Management in New York Organic Apples.

Organic apple production in the eastern US is small and is mostly based on existing varieties, which are susceptible to scab, and rootstocks, which are susceptible to fire blight. This requires numerous sprays per year of various pesticides to produce acceptable fruit. From 2014 to 2016, we tested different arthropod, disease and weed management programs in an advanced tall spindle high-density production system that included disease-resistant cultivars and rootstocks, in an organic research planting of apples in Geneva, New York. Arthropod and disease management regimens were characterized as Advanced Organic, Minimal Organic, or Untreated Control. Results varied by year and variety, but, in general, the Advanced program was more effective than the Minimal program in preventing damage from internal-feeding Lepidoptera, plum curculio, and obliquebanded leafroller, and less effective than the Minimal program against damage by foliar insects. Both organic programs provided comparable control of sooty blotch, cedar apple rust, and fire blight, with some variability across cultivars and years. The advanced selection CC1009 and Modi seemed to possess complete resistance to cedar apple rust, while Pristine had partial resistance. For weed control, bark chip mulch, organic soap sprays, and limonene sprays tended to be most effective, while mechanical tillage and flame weeding had lower success.