SKA2 regulated hyperactive secretory autophagy drives neuroinflammation-induced neurodegeneration

High levels of proinflammatory cytokines induce neurotoxicity and catalyze inflammation-driven neurodegeneration, but the specific release mechanisms from microglia remain elusive. Here we show that secretory autophagy (SA), a non-lytic modality of autophagy for secretion of vesicular cargo, regulates neuroinflammation-mediated neurodegeneration via SKA2 and FKBP5 signaling. SKA2 inhibits SA-dependent IL-1β release by counteracting FKBP5 function. Hippocampal Ska2 knockdown in male mice hyperactivates SA resulting in neuroinflammation, subsequent neurodegeneration and complete hippocampal atrophy within six weeks. The hyperactivation of SA increases IL-1β release, contributing to an inflammatory feed-forward vicious cycle including NLRP3-inflammasome activation and Gasdermin D-mediated neurotoxicity, which ultimately drives neurodegeneration. Results from protein expression and co-immunoprecipitation analyses of male and female postmortem human brains demonstrate that SA is hyperactivated in Alzheimer's disease. Overall, our findings suggest that SKA2-regulated, hyperactive SA facilitates neuroinflammation and is linked to Alzheimer's disease, providing mechanistic insight into the biology of neuroinflammation

Predator traits determine food-web architecture across ecosystems

Predator–prey interactions in natural ecosystems generate complex food webs that have a simple universal body-size architecture where predators are systematically larger than their prey. Food-web theory shows that the highest predator–prey body-mass ratios found in natural food webs may be especially important because they create weak interactions with slow dynamics that stabilize communities against perturbations and maintain ecosystem functioning. Identifying these vital interactions in real communities typically requires arduous identification of interactions in complex food webs. Here, we overcome this obstacle by developing predator-trait models to predict average body-mass ratios based on a database comprising 290 food webs from freshwater, marine and terrestrial ecosystems across all continents. We analysed how species traits constrain body-size architecture by changing the slope of the predator–prey body-mass scaling. Across ecosystems, we found high body-mass ratios for predator groups with specific trait combinations including (1) small vertebrates and (2) large swimming or flying predators. Including the metabolic and movement types of predators increased the accuracy of predicting which species are engaged in high body-mass ratio interactions. We demonstrate that species traits explain striking patterns in the body-size architecture of natural food webs that underpin the stability and functioning of ecosystems, paving the way for community-level management of the most complex natural ecosystems

Analyzing pathogen suppressiveness in bioassays with natural soils using integrative maximum likelihood methods in R

The potential of soils to naturally suppress inherent plant pathogens is an important ecosystem function. Usually, pathogen infection assays are used for estimating the suppressive potential of soils. In natural soils, however, co-occurring pathogens might simultaneously infect plants complicating the estimation of a focal pathogen’s infection rate (initial slope of the infection-curve) as a measure of soil suppressiveness. Here, we present a method in R correcting for these unwanted effects by developing a two pathogen mono-molecular infection model. We fit the two pathogen mono-molecular infection model to data by using an integrative approach combining a numerical simulation of the model with an iterative maximum likelihood fit. We show that in presence of co-occurring pathogens using uncorrected data leads to a critical under- or overestimation of soil suppressiveness measures. In contrast, our new approach enables to precisely estimate soil suppressiveness measures such as plant infection rate and plant resistance time. Our method allows a correction of measured infection parameters that is necessary in case different pathogens are present. Moreover, our model can be (1) adapted to use other models such as the logistic or the Gompertz model; and (2) it could be extended by a facilitation parameter if infections in plants increase the susceptibility to new infections. We propose our method to be particularly useful for exploring soil suppressiveness of natural soils from different sites (e.g., in biodiversity experiments)

Analyzing pathogen suppressiveness in bioassays with natural soils using integrative maximum likelihood methods in R

ABSTRACT The potential of soils to naturally suppress inherent plant pathogens is an important ecosystem function. Usually, pathogen infection assays are used for estimating the suppressive potential of soils. In natural soils, however, co-occurring pathogens might simultaneously infect plants complicating the estimation of a focal pathogen's infection rate (initial slope of the infection-curve) as a measure of soil suppressiveness. Here, we present a method in R correcting for these unwanted effects by developing a two pathogen mono-molecular infection model. We fit the two pathogen mono-molecular infection model to data by using an integrative approach combining a numerical simulation of the model with an iterative maximum likelihood fit. We show that in presence of co-occurring pathogens using uncorrected data leads to a critical under-or overestimation of soil suppressiveness measures. In contrast, our new approach enables to precisely estimate soil suppressiveness measures such as plant infection rate and plant resistance time. Our method allows a correction of measured infection parameters that is necessary in case different pathogens are present. Moreover, our model can be (1) adapted to use other models such as the logistic or the Gompertz model; and (2) it could be extended by a facilitation parameter if infections in plants increase the susceptibility to new infections. We propose our method to be particularly useful for exploring soil suppressiveness of natural soils from different sites (e.g., in biodiversity experiments)

Protozoa stimulate the plant beneficial activity of rhizospheric pseudomonads

Aims: The functioning of plant-associated bacteria is strongly influenced by their interaction with other organisms. For instance, bacteria upregulate the production of secondary metabolites in presence of protozoa and we hypothesised that this interaction may contribute to plant health. Methods: Here, we tested if the effect of beneficial pseudomonads on wheat growth and health is modified by co-inoculation with the bacterivorous amoeba Acanthamoeba castellanii. We assessed effects of this co-inoculation in absence and presence of the root pathogen Pythium ultimum. Results: In absence of amoebae, bacterial isolates had few beneficial effects and some isolates exacerbated growth inhibition by the pathogen (despite their reported beneficial effects in vitro). Effects on plant growth in absence and presence of the pathogen were negatively correlated. Co-inoculation with amoebae suppressed this relationship, leading to plant growth promotion in absence and reduction of deleterious effects in presence of the pathogen. The positive effect of amoebae in absence of the pathogen could be related to bacterial siderophore production in vitro. Conclusions: Our results illustrate the discrepancy between in vitro and in vivo effects of plant beneficial bacteria. Incorporation of other rhizospheric trophic components such as protists may be a key factor to influence the plant-beneficial potential of bacteria in vivo

Unravelling Linkages between Plant Community Composition and the Pathogen-Suppressive Potential of Soils

Plant diseases cause dramatic yield losses worldwide. Current disease control practices can be deleterious for the environment and human health, calling for alternative and sustainable management regimes. Soils harbour microorganisms that can efficiently suppress pathogens. Uncovering mediators driving their functioning in the field still remains challenging, but represents an essential step in order to develop strategies for increased soil health. We set up plant communities of varying richness to experimentally test the potential of soils differing in plant community history to suppress the pathogen Rhizoctonia solani. The results indicate that plant communities shape soil-disease suppression via changes in abiotic soil properties and the abundance of bacterial groups including species of the genera Actinomyces, Bacillus and Pseudomonas. Further, the results suggest that pairwise interactions between specific plant species strongly affect soil suppressiveness. Using structural equation modelling, we provide a pathway orientated framework showing how the complex interactions between plants, soil and microorganisms jointly shape soil suppressiveness. Our results stress the importance of plant community composition as a determinant of soil functioning, such as the disease suppressive potential of soils

Unravelling Linkages between Plant Community Composition and the Pathogen-Suppressive Potential of Soils

Plant diseases cause dramatic yield losses worldwide. Current disease control practices can be deleterious for the environment and human health, calling for alternative and sustainable management regimes. Soils harbour microorganisms that can efficiently suppress pathogens. Uncovering mediators driving their functioning in the field still remains challenging, but represents an essential step in order to develop strategies for increased soil health. We set up plant communities of varying richness to experimentally test the potential of soils differing in plant community history to suppress the pathogen Rhizoctonia solani. The results indicate that plant communities shape soil-disease suppression via changes in abiotic soil properties and the abundance of bacterial groups including species of the genera Actinomyces, Bacillus and Pseudomonas. Further, the results suggest that pairwise interactions between specific plant species strongly affect soil suppressiveness. Using structural equation modelling, we provide a pathway orientated framework showing how the complex interactions between plants, soil and microorganisms jointly shape soil suppressiveness. Our results stress the importance of plant community composition as a determinant of soil functioning, such as the disease suppressive potential of soils

Data from: Bacterial diversity amplifies nutrient-based plant-soil feedbacks

Plants foster diverse assemblages of bacteria in the rhizosphere serving important functions which may result in enhanced plant growth. Microbial diversity is increasingly recognized to shape the functionality of microbial communities. This leads to the assumption that there is a positive relationship between rhizosphere diversity and plant growth. Here we investigate how bacterial diversity affects the mineralization of organic matter and plant nutrient acquisition. We hypothesized that altered bacterial diversity will affect nitrogen mineralisation, uptake by plants and ultimately plant growth. We set up a controlled model system with Arabidopsis thaliana colonized by defined assemblages of fluorescent pseudomonads, a well-characterised plant-beneficial rhizosphere taxon. The growth substrate contained casein as sole nitrogen source, making the plant nitrogen uptake dependant on breakdown by bacterial enzymes. Bacterial diversity was associated with a higher enzyme activity which increased nitrogen mineralization and enhanced plant growth. The effect of bacterial diversity on plant growth increased with time, pointing to a positive feedback between bacteria and plants: Bigger plants associated with species-rich bacterial communities supported more bacterial growth, which further enhanced the impact of bacteria on plant growth. We demonstrate that plant-soil feedbacks establish rapidly during one single growth season and that bacterial diversity modulates this interaction. Preserving soil microbial diversity therefore may improve positive plant-soil feedbacks and thereby plant growth