Exploring the role of fungal endophytes in the sudden death syndrome of the invasive shrub Chrysanthemoides monilifera subsp. rotundata in Australia

Pathogens that attack invasive plants can positively affect the integrity and functioning of ecosystems. Stem-tip dieback and extensive wilting followed by sudden death have been observed in Chrysanthemoides monilifera subsp. rotundata (bitou bush), one of Australia’s worst invasive shrubs. Metabarcoding and culturing methods were used to investigate if fungi are implicated in this syndrome. Metabarcoding results revealed significantly different endophytic fungal communities within healthy and diseased bitou bush, and co-located native plants. There was no difference in fungal communities between soil sampled in the root zone of healthy and diseased bitou bush at the same site. Two Diaporthe sp. operational taxonomic units (OTUs), dominant at sites with extensive wilting, explained 30% of the similarity between diseased bitou bush across all sites. Two other OTUs, Austropleospora osteospermi and Coprinellus sp., explained 20 and 40% of the similarity between diseased plants, respectively, and were only dominant at sites with dead or stunted, partially defoliated but not wilted bitou bush. A Penicillium sp. OTU explained 90% of the similarity between healthy bitou bush. Various Diaporthe spp. dominated isolations from diseased bitou bush. Manipulative experiments confirmed Diaporthe spp. pathogenicity on bitou bush excised and in-situ stems. In another experiment, Diaporthe masirevicii infected flowers and from there colonized stems endophytically, but wilting and sudden death of bitou bush did not occur within the experimental timeframe. Our study provides circumstantial evidence that bitou bush sudden death syndrome is the result of a shift in the composition of its endophytic fungal community, from mutualist to pathogenic species.

Role of cardiac hypoxia in the pathogenesis of sudden death syndrome in broiler chickens – A metabolic and molecular study

Sudden death syndrome (SDS) is an economically important disorder in broiler chickens with unknown aetiology. The aim of the present study was to evaluate the metabolic and molecular alterations related to hypoxia in the myocardium of broiler chickens with SDS. Samples from the cardiac muscle of internal control broiler chickens (ICs) (n = 36) and chickens having died of SDS (n = 36) were obtained during the rearing period. The activities of lactate dehydrogenase (LDH) and creatine phosphokinase (CPK) and the concentration of lactate were measured in the cardiac tissue using available commercial kits. The expression of hypoxia-inducing factor 1α (HIF1α), glucose transporter 1 (GLUT1), pyruvate dehydrogenase kinase 4 (PDHK4) and monocarboxylate transporter 4 (MCT4) genes was determined in the myocardium by real-time PCR analysis. The results showed the elevation of lactate level and activities of LDH and CPK in the cardiac muscle of SDS-affected chickens compared with the IC birds (P < 0.05). The cardiac muscle expression of HIF1α, MCT4 and GLUT1 genes was increased, while the PDHK4 mRNA level was decreased in the SDS-affected group compared to those in the IC chickens (P < 0.05). Our results showed that metabolic remodelling associated with hypoxia in the cardiac tissues may have an important role in the pathogenesis of cardiac insufficiency and SDS in broiler chickens.

Economic Impact of Fluopyram-Amended Seed Treatments to Reduce Soybean Yield Loss Associated with Sudden Death Syndrome

Soybean (Glycine max) sudden death syndrome (SDS), caused by Fusarium virguliforme, is a key limitation in reaching soybean yield potential, stemming from incomplete disease management through cultural practices and partial host resistance. A fungicidal seed treatment was released in 2014 with the active ingredient fluopyram and was the first chemical management strategy to reduce soybean yield loss stemming from SDS. Although farm level studies have found fluopyram profitable, we were curious to discover whether fluopyram would be beneficial nationally if targeted to soybean fields at risk for SDS yield loss. To estimate economic benefits of fluopyram adoption in SDS at-risk acres, in the light of U.S. public research and outreach from a privately developed product, we applied an economic surplus approach, calculating ex ante net benefits from 2018 to 2032. Through this framework of logistic adoption of fluopyram for alleviation of SDS-associated yield losses, we projected a net benefit of $5.8 billion over 15 years, considering the costs of public seed treatment research and future extension communication. Although the sensitivity analysis indicates that overall net benefits from fluopyram adoption on SDS at-risk acres are highly dependent upon the market price of soybean, the incidence of SDS, the adoption path, and ceiling of this seed treatment, the net benefits still exceeded $407 million in the worst-case scenario.

A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.