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Water is essential to life and thus, an essential resource. However, freshwater resources are limited and their maintenance is crucial. Pollution with chemicals and pathogens through urbanization and a growing population impair the quality of freshwater. Furthermore, water can serve as vector for the transmission of pathogens resulting in water-borne illness.
The Interdisciplinary Research Group III – "Water" of the Leibniz alliance project INFECTIONS‘21 investigated water as a hub for pathogens focusing on Clostridioides difficile and avian influenza A viruses that may be shed into the water. Another aim of this study was to characterize the bacterial communities in a wastewater treatment plant (WWTP) of the capital Berlin, Germany to further assess potential health risks associated with wastewater management practices.
Bacterial communities of WWTP inflow and effluent differed significantly. The proportion of fecal/enteric bacteria was relatively low and OTUs related to potential enteric pathogens were largely removed from inflow to effluent. However, a health risk might exist as an increased relative abundance of potential pathogenic Legionella spp. such as L. lytica was observed. Three Clostridioides difficile isolates from wastewater inflow and an urban bathing lake in Berlin (‗Weisser See‘) were obtained and sequenced. The two isolates from the wastewater did not carry toxin genes, whereas the isolate from the lake was positive for the toxin genes. All three isolates were closely related to human strains. This indicates a potential, but rather sporadic health risk. Avian influenza A viruses were detected in 38.8% of sediment samples by PCR, but virus isolation failed. An experiment with inoculated freshwater and sediment samples showed that virus isolation from sediment requires relatively high virus concentrations and worked much better in Madin-Darby Canine Kidney (MDCK) cell cultures than in embryonated chicken eggs, but low titre of influenza contamination in freshwater samples was sufficient to recover virus.
In conclusion, this work revealed potential health risks coming from bacterial groups with pathogenic potential such as Legionella spp. whose relative abundance is higher in the released effluent than in the inflow of the investigated WWTP. It further indicates that water bodies such as wastewater and lake sediments can serve as reservoir and vector, even for non-typical water-borne or water-transmitted pathogens such as C. difficile.
Responses to pathogens, including host transcriptional reprogramming, require partially antagonistic signalling pathways dependent on the phytohormones salicylic (SA) and jasmonic (JA) acids. However, upstream factors modulating the interplay of these pathways are not well characterized. Here, we identify the transcription factor ANAC032 from Arabidopsis thaliana as one such regulator in response to the bacterial pathogen Pseudomonas syringae pv. tomato DC3000 (Pst). ANAC032 directly represses MYC2 activation upon Pst attack, resulting in blockage of coronatine-mediated stomatal reopening which restricts entry of bacteria into plant tissue. Furthermore, ANAC032 activates SA signalling by repressing NIMIN1, a key negative regulator of SA-dependent defence. Finally, ANAC032 reduces expression of JA-responsive genes, including PDF1.2A. Thus, ANAC032 enhances resistance to Pst by generating an orchestrated transcriptional output towards key SA- and JA-signalling genes coordinated through direct binding of ANAC032 to the MYC2, NIMIN1 and PDF1.2A promoters.
Phytohormones act in concert to coordinate plant growth and the response to environmental cues. Gibberellins (GAs) are growth-promoting hormones that recently emerged as modulators of plant immune signaling. By regulating the stability of DELLA proteins, GAs intersect with the signaling pathways of the classical primary defense hormones, salicylic acid (SA) and jasmonic acid (JA), thereby altering the final outcome of the immune response. DELLA proteins confer resistance to necrotrophic pathogens by potentiating JA signaling and raise the susceptibility to biotrophic pathogens by attenuating the SA pathway. Here, we show that JUB1, a core element of the GA - brassinosteroid (BR) - DELLA regulatory module, functions as a negative regulator of defense responses against Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) and mediates the crosstalk between growth and immunity.
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.