A meta-analysis on global change drivers and the risk of infectious disease.

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors1. Studies have shown that infectious disease risk is modified by changes to biodiversity2-6, climate change7-11, chemical pollution12-14, landscape transformations15-20 and species introductions21. However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host-parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

Stochastic modeling of Dalbulus maidis, vector of maize diseases.

We developed a simple linear stochastic model for Dalbulus maidis dependent exclusively on temperature, whose parameters were determined from published field and laboratory studies performed at different temperatures. This model takes into account the principal stages and events of the life cycle of this pest, which is vector of maize diseases. We implemented the effect of distributed delays or Linear Chain Trick (LCT) considering a fixed number of sub-stages for egg and nymph stages of Dalbulus maidis in order to accurately represent what is observed in nature. A sensitivity analysis allows us to observe that the speed of the dynamics is sensitive to changes in the development rates, but not to the longevity of each stage or the fecundity, which almost exclusively affect insect abundance. We used our model to study its predictive and explanatory capacity considering a published experiment as a case study. Although the simulation results show a behavior qualitatively equivalent to that observed in the experimental results it is not possible to explain accurately the magnitude, nor the times in which the maximum abundances of second-generation nymphs and adults are reached. Therefore, we evaluated three possible scenarios for the insect that allow us to glimpse some of the advantages of having a computational model in order to find out what processes, taken into account in the model, may explain the differences observed between published experimental results and model results. The three proposed scenarios, based on variations in the parameterized rates of the model, can satisfactorily explain the experimental observations. We observed that in order to better simulate the experimental results it is not necessary to modify fecundity or mortality rates. However, it is necessary to accelerate the average development rates of our model by 20 to 40 %, compatible with extreme values of the rates close to the upper edges of the confidence bands of our parameterization rate curves, according to insects with faster development rates already reported in literature.

Transcriptional profiling reveals a critical role of GmFT2a in soybean staygreen syndrome caused by the pest Riptortus pedestris.

Soybean staygreen syndrome, characterized by delayed leaf and stem senescence, abnormal pods, and aborted seeds, has recently become a serious and prominent problem in soybean production. Although the pest Riptortus pedestris has received increasing attention as the possible cause of staygreen syndrome, the mechanism remains unknown. Here, we clarify that direct feeding by R. pedestris, not transmission of a pathogen by this pest, is the primary cause of typical soybean staygreen syndrome and that critical feeding damage occurs at the early pod stage. Transcriptome profiling of soybean indicated that many signal transduction pathways, including photoperiod, hormone, defense response, and photosynthesis, respond to R. pedestris infestation. Importantly, we discovered that members of the FLOWERING LOCUS T (FT) gene family were suppressed by R. pedestris infestation, and overexpression of floral inducer GmFT2a attenuates staygreen symptoms by mediating soybean defense response and photosynthesis. Together, our findings systematically illustrate the association between pest infestation and soybean staygreen syndrome and provide the basis for establishing a targeted soybean pest prevention and control system.

Differential Transmission of Old and New World Begomoviruses by Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) Cryptic Species of Bemisia tabaci.

Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) are two of the most invasive members of the sweetpotato whitefly, Bemisia tabaci, cryptic species complexes and are efficient vectors of begomoviruses. Bemisia tabaci MEAM1 is the predominant vector of begomoviruses in open-field vegetable crops in the southeastern United States. However, recently B. tabaci MED also has been detected in the landscape outside of greenhouses in Florida and Georgia. This study compared the transmission efficiency of one Old-World (OW) and two New-World (NW) begomoviruses prevalent in the southeastern United States, viz., tomato yellow leaf curl virus (TYLCV), cucurbit leaf crumple virus (CuLCrV), and sida golden mosaic virus (SiGMV) between B. tabaci MEAM1 and B. tabaci MED. Bemisia tabaci MEAM1 efficiently transmitted TYLCV, CuLCrV, or SiGMV, whereas B. tabaci MED only transmitted TYLCV. Percent acquisition and retention of OW TYLCV following a 72 h acquisition access period was significantly higher for B. tabaci MED than B. tabaci MEAM1. In contrast, B. tabaci MEAM1 acquired and retained significantly more NW bipartite begomoviruses, CuLCrV or SiGMV, than B. tabaci MED. Quantitative analysis (qPCR) of virus DNA in whitefly internal tissues revealed reduced accumulation of CuLCrV or SiGMV in B. tabaci MED than in B. tabaci MEAM1. Fluorescent in situ hybridization (FISH) showed localization of CuLCrV or SiGMV in the midgut of B. tabaci MED and B. tabaci MEAM1. However, localization of CuLCrV or SiGMV was only observed in the primary salivary glands of B. tabaci MEAM1 and not B. tabaci MED. TYLCV localization was observed in all internal tissues of B. tabaci MEAM1 and B. tabaci MED. Overall, results demonstrate that both B. tabaci MEAM1 and B. tabaci MED are efficient vectors of OW TYLCV. However, for the NW begomoviruses, CuLCrV and SiGMV, B. tabaci MEAM1 seems to a better vector.

A method of detecting apple leaf diseases based on improved convolutional neural network.

Apple tree diseases have perplexed orchard farmers for several years. At present, numerous studies have investigated deep learning for fruit and vegetable crop disease detection. Because of the complexity and variety of apple leaf veins and the difficulty in judging similar diseases, a new target detection model of apple leaf diseases DF-Tiny-YOLO, based on deep learning, is proposed to realize faster and more effective automatic detection of apple leaf diseases. Four common apple leaf diseases, including 1,404 images, were selected for data modeling and method evaluation, and made three main improvements. Feature reuse was combined with the DenseNet densely connected network and further realized to reduce the disappearance of the deep gradient, thus strengthening feature propagation and improving detection accuracy. We introduced Resize and Re-organization (Reorg) and conducted convolution kernel compression to reduce the calculation parameters of the model, improve the operating detection speed, and allow feature stacking to achieve feature fusion. The network terminal uses convolution kernels of 1 × 1, 1 × 1, and 3 × 3, in turn, to realize the dimensionality reduction of features and increase network depth without increasing computational complexity, thus further improving the detection accuracy. The results showed that the mean average precision (mAP) and average intersection over union (IoU) of the DF-Tiny-YOLO model were 99.99% and 90.88%, respectively, and the detection speed reached 280 FPS. Compared with the Tiny-YOLO and YOLOv2 network models, the new method proposed in this paper significantly improves the detection performance. It can also detect apple leaf diseases quickly and effectively.

Virus Diseases of Cereal and Oilseed Crops in Australia: Current Position and Future Challenges.

This review summarizes research on virus diseases of cereals and oilseeds in Australia since the 1950s. All viruses known to infect the diverse range of cereal and oilseed crops grown in the continent's temperate, Mediterranean, subtropical and tropical cropping regions are included. Viruses that occur commonly and have potential to cause the greatest seed yield and quality losses are described in detail, focusing on their biology, epidemiology and management. These are: barley yellow dwarf virus, cereal yellow dwarf virus and wheat streak mosaic virus in wheat, barley, oats, triticale and rye; Johnsongrass mosaic virus in sorghum, maize, sweet corn and pearl millet; turnip yellows virus and turnip mosaic virus in canola and Indian mustard; tobacco streak virus in sunflower; and cotton bunchy top virus in cotton. The currently less important viruses covered number nine infecting nine cereal crops and 14 infecting eight oilseed crops (none recorded for rice or linseed). Brief background information on the scope of the Australian cereal and oilseed industries, virus epidemiology and management and yield loss quantification is provided. Major future threats to managing virus diseases effectively include damaging viruses and virus vector species spreading from elsewhere, the increasing spectrum of insecticide resistance in insect and mite vectors, resistance-breaking virus strains, changes in epidemiology, virus and vectors impacts arising from climate instability and extreme weather events, and insufficient industry awareness of virus diseases. The pressing need for more resources to focus on addressing these threats is emphasized and recommendations over future research priorities provided.

Biochemistry of Terpenes and Recent Advances in Plant Protection.

Biodiversity is adversely affected by the growing levels of synthetic chemicals released into the environment due to agricultural activities. This has been the driving force for embracing sustainable agriculture. Plant secondary metabolites offer promising alternatives for protecting plants against microbes, feeding herbivores, and weeds. Terpenes are the largest among PSMs and have been extensively studied for their potential as antimicrobial, insecticidal, and weed control agents. They also attract natural enemies of pests and beneficial insects, such as pollinators and dispersers. However, most of these research findings are shelved and fail to pass beyond the laboratory and greenhouse stages. This review provides an overview of terpenes, types, biosynthesis, and their roles in protecting plants against microbial pathogens, insect pests, and weeds to rekindle the debate on using terpenes for the development of environmentally friendly biopesticides and herbicides.

Widespread mortality of trembling aspen (Populus tremuloides) throughout interior Alaskan boreal forests resulting from a novel canker disease.

Over the past several decades, growth declines and mortality of trembling aspen throughout western Canada and the United States have been linked to drought, often interacting with outbreaks of insects and fungal pathogens, resulting in a "sudden aspen decline" throughout much of aspen's range. In 2015, we noticed an aggressive fungal canker causing widespread mortality of aspen throughout interior Alaska and initiated a study to quantify potential drivers for the incidence, virulence, and distribution of the disease. Stand-level infection rates among 88 study sites distributed across 6 Alaska ecoregions ranged from <1 to 69%, with the proportion of trees with canker that were dead averaging 70% across all sites. The disease is most prevalent north of the Alaska Range within the Tanana Kuskokwim ecoregion. Modeling canker probability as a function of ecoregion, stand structure, landscape position, and climate revealed that smaller-diameter trees in older stands with greater aspen basal area have the highest canker incidence and mortality, while younger trees in younger stands appear virtually immune to the disease. Sites with higher summer vapor pressure deficits had significantly higher levels of canker infection and mortality. We believe the combined effects of this novel fungal canker pathogen, drought, and the persistent aspen leaf miner outbreak are triggering feedbacks between carbon starvation and hydraulic failure that are ultimately driving widespread mortality. Warmer early-season temperatures and prolonged late summer drought are leading to larger and more severe wildfires throughout interior Alaska that are favoring a shift from black spruce to forests dominated by Alaska paper birch and aspen. Widespread aspen mortality fostered by this rapidly spreading pathogen has significant implications for successional dynamics, ecosystem function, and feedbacks to disturbance regimes, particularly on sites too dry for Alaska paper birch.

Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis.

Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea fields. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven different locations in India with combination of low soil moisture and pathogen stress treatments confirmed the impact of low soil moisture on DRR incidence under different agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specific prediction of DRR incidence, enabling efficient decision-making in chickpea cultivation to minimize yield loss.

A Phosphonate Natural Product Made by Pantoea ananatis is Necessary and Sufficient for the Hallmark Lesions of Onion Center Rot.

Pantoea ananatis is the primary cause of onion center rot. Genetic data suggest that a phosphonic acid natural product is required for pathogenesis; however, the nature of the molecule is unknown. Here, we show that P. ananatis produces at least three phosphonates, two of which were purified and structurally characterized. The first, designated pantaphos, was shown to be 2-(hydroxy[phosphono]methyl)maleate; the second, a probable biosynthetic precursor, was shown to be 2-(phosphonomethyl)maleate. Purified pantaphos is both necessary and sufficient for the hallmark lesions of onion center rot. Moreover, when tested against mustard seedlings, the phytotoxic activity of pantaphos was comparable to the widely used herbicides glyphosate and phosphinothricin. Pantaphos was also active against a variety of human cell lines but was significantly more toxic to glioblastoma cells. Pantaphos showed little activity when tested against a variety of bacteria and fungi.IMPORTANCEPantoea ananatis is a significant plant pathogen that targets a number of important crops, a problem that is compounded by the absence of effective treatments to prevent its spread. Our identification of pantaphos as the key virulence factor in onion center rot suggests a variety of approaches that could be employed to address this significant plant disease. Moreover, the general phytotoxicity of the molecule suggests that it could be developed into an effective herbicide to counter the alarming rise in herbicide-resistant weeds.

Genetic variation for tolerance to the downy mildew pathogen Peronospora variabilis in genetic resources of quinoa (Chenopodium quinoa).

BACKGROUND: Quinoa (Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding. RESULTS: We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance. CONCLUSIONS: The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.

Cadmium Stress Reprograms ROS/RNS Homeostasis in Phytophthora infestans (Mont.) de Bary.

Heavy metal pollution causes many soils to become a toxic environment not only for plants, but also microorganisms; however, little is known how heavy metal contaminated environment affects metabolism of phytopathogens and their capability of infecting host plants. In this study the oomycete Phytophthora infestans (Mont.) de Bary, the most harmful pathogen of potato, growing under moderate cadmium stress (Cd, 5 mg/L) showed nitro-oxidative imbalance associated with an enhanced antioxidant response. Cadmium notably elevated the level of nitric oxide, superoxide and peroxynitrite that stimulated nitrative modifications within the RNA and DNA pools in the phytopathogen structures. In contrast, the protein pool undergoing nitration was diminished confirming that protein tyrosine nitration is a flexible element of the oomycete adaptive strategy to heavy metal stress. Finally, to verify whether Cd is able to modify P. infestans pathogenicity, a disease index and molecular assessment of disease progress were analysed indicating that Cd stress enhanced aggressiveness of vr P. infestans towards various potato cultivars. Taken together, Cd not only affected hyphal growth rate and caused biochemical changes in P. infestans structures, but accelerated the pathogenicity as well. The nitro-oxidative homeostasis imbalance underlies the phytopathogen adaptive strategy and survival in the heavy metal contaminated environment.

Climate change and disease in plant communities.

Climate change is triggering similar effects on the incidence and severity of disease for crops in agriculture and wild plants in natural communities. The complexity of natural ecosystems, however, generates a complex array of interactions between wild plants and pathogens in marked contrast to those generated in the structural and species simplicity of most agricultural crops. Understanding the different impacts of climate change on agricultural and natural ecosystems requires accounting for the specific interactions between an individual pathogen and its host(s) and their subsequent effects on the interplay between the host and other species in the community. Ultimately, progress will require looking past short-term fluctuations to multiyear trends to understand the nature and extent of plant and pathogen evolutionary adaptation and determine the fate of plants under future climate change.

A multi-omics approach to solving problems in plant disease ecology.

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.

Genome-wide identification of the Sec-dependent secretory protease genes in Erwinia amylovora and analysis of their expression during infection of immature pear fruit.

The general secretory (Sec) pathway represents a common mechanism by which bacteria secrete proteins, including virulence factors, into the extracytoplasmic milieu. However, there is little information about this system, as well as its associated secretory proteins, in relation to the fire blight pathogen Erwinia amylovora. In this study, data mining revealed that E. amylovora harbors all of the essential components of the Sec system. Based on this information, we identified putative Sec-dependent secretory proteases in E. amylovora on a genome-wide scale. Using the programs SignalP, LipoP, and Phobius, a total of 15 putative proteases were predicted to contain the N-terminal signal peptides (SPs) that might link them to the Sec-dependent pathway. The activities of the predicted SPs were further validated using an Escherichia coli-based alkaline phosphatase (PhoA) gene fusion system that confirmed their extracytoplasmic property. Transcriptional analyses showed that the expression of 11 of the 15 extracytoplasmic protease genes increased significantly when E. amylovora was used to inoculate immature pears, suggesting their potential roles in plant infection. The results of this study support the suggestion that E. amylovora might employ the Sec system to secrete a suite of proteases to enable successful infection of plants, and shed new light on the interaction of E. amylovora with host plants.

Functional variation in phyllogen, a phyllody-inducing phytoplasma effector family, attributable to a single amino acid polymorphism.

Flower malformation represented by phyllody is a common symptom of phytoplasma infection induced by a novel family of phytoplasma effectors called phyllogens. Despite the accumulation of functional and structural phyllogen information, the molecular mechanisms of phyllody have not yet been integrated with their evolutionary aspects due to the limited data on their homologs across diverse phytoplasma lineages. Here, we developed a novel universal PCR-based approach to identify 25 phytoplasma phyllogens related to nine "Candidatus Phytoplasma" species, including four species whose phyllogens have not yet been identified. Phylogenetic analyses showed that the phyllogen family consists of four groups (phyl-A, -B, -C, and -D) and that the evolutionary relationships of phyllogens were significantly distinct from those of phytoplasmas, suggesting that phyllogens were transferred horizontally among phytoplasma strains and species. Although phyllogens belonging to the phyl-A, -C, and -D groups induced phyllody, the phyl-B group lacked the ability to induce phyllody. Comparative functional analyses of phyllogens revealed that a single amino acid polymorphism in phyl-B group phyllogens prevented interactions between phyllogens and A- and E-class MADS domain transcription factors (MTFs), resulting in the inability to degrade several MTFs and induce phyllody. Our finding of natural variation in the function of phytoplasma effectors provides new insights into molecular mechanisms underlying the aetiology of phytoplasma diseases.

Mass spectrometry imaging as a potential technique for diagnostic of Huanglongbing disease using fast and simple sample preparation.

Huanglongbing (HLB) is a disease of worldwide incidence that affects orange trees, among other commercial varieties, implicating in great losses to the citrus industry. The disease is transmitted through Diaphorina citri vector, which inoculates Candidatus Liberibacter spp. in the plant sap. HLB disease lead to blotchy mottle and fruit deformation, among other characteristic symptoms, which induce fruit drop and affect negatively the juice quality. Nowadays, the disease is controlled by eradication of sick, symptomatic plants, coupled with psyllid control. Polymerase chain reaction (PCR) is the technique most used to diagnose the disease; however, this methodology involves high cost and extensive sample preparation. Mass spectrometry imaging (MSI) technique is a fast and easily handled sample analysis that, in the case of Huanglongbing allows the detection of increased concentration of metabolites associated to the disease, including quinic acid, phenylalanine, nobiletin and sucrose. The metabolites abieta-8,11,13-trien-18-oic acid, suggested by global natural product social molecular networking (GNPS) analysis, and 4-acetyl-1-methylcyclohexene showed a higher distribution in symptomatic leaves and have been directly associated to HLB disease. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) allows the rapid and efficient detection of biomarkers in sweet oranges infected with Candidatus Liberibacter asiaticus and can be developed into a real-time, fast-diagnostic technique.

Robustness of plant quantitative disease resistance is provided by a decentralized immune network.

Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR in Arabidopsis thaliana in response to the bacterial pathogen Xanthomonas campestris To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression of RKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistance to XcampestrisRKS1-dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized in Athaliana Protein-protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.