Soil microbiome indicators can predict crop growth response to large-scale inoculation with arbuscular mycorrhizal fungi

Alternative solutions to mineral fertilizers and pesticides that reduce the environmental impact of agriculture are urgently needed. Arbuscular mycorrhizal fungi (AMF) can enhance plant nutrient uptake and reduce plant stress; yet, large-scale field inoculation trials with AMF are missing, and so far, results remain unpredictable. We conducted on-farm experiments in 54 fields in Switzerland and quantified the effects on maize growth. Growth response to AMF inoculation was highly variable, ranging from -12% to +40%. With few soil parameters and mainly soil microbiome indicators, we could successfully predict 86% of the variation in plant growth response to inoculation. The abundance of pathogenic fungi, rather than nutrient availability, best predicted (33%) AMF inoculation success. Our results indicate that soil microbiome indicators offer a sustainable biotechnological perspective to predict inoculation success at the beginning of the growing season. This predictability increases the profitability of microbiome engineering as a tool for sustainable agricultural management

Predicting soil fungal communities from chemical and physical properties

Introduction: Biogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the diversity of soil microbes is largely controlled by edaphic environmental variables, microbiome community prediction from soil properties has received less attention. In this study, we specifically investigated whether it is possible to predict the composition of soil fungal communities based on physicochemical soil data using multivariate ordination. Materials and Methods: We sampled soil from 59 arable fields in Switzerland and assembled paired data of physicochemical soil properties as well as profiles of soil fungal communities. Fungal communities were characterized using long-read sequencing of the entire ribosomal internal transcribed spacer. We used redundancy analysis to combine the physical and chemical soil measurements with the fungal community data. Results: We identified a reduced set of 10 soil properties that explained fungal community composition. Soil properties with the strongest impact on the fungal community included pH, potassium and sand content. Finally, we evaluated the model for its suitability for prediction using leave-one-out validation. The prediction of community composition was successful for most soils, and only 3/59 soils could not be well predicted (Pearson correlation coefficients between observed and predicted communities of <0.5). Further, we successfully validated our prediction approach with a publicly available data set. With both data sets, prediction was less successful for soils characterized by very unique properties or diverging fungal communities, while it was successful for soils with similar characteristics and microbiome. Conclusions: Reliable prediction of microbial communities from chemical soil properties could bypass the complex and laborious sequencing-based generation of microbiota data, thereby making soil microbiome information available for agricultural purposes such as pathogen monitoring, field inoculation or yield projections

Relative qPCR to quantify colonization of plant roots by arbuscular mycorrhizal fungi

Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses

Plant secondary metabolite-dependent plant-soil feedbacks can improve crop yield in the field.

Plant secondary metabolites that are released into the rhizosphere alter biotic and abiotic soil properties, which in turn affect the performance of other plants. How this type of plant-soil feedback affects agricultural productivity and food quality in the field in the context of crop rotations is unknown. Here, we assessed the performance, yield and food quality of three winter wheat varieties growing in field plots whose soils had been conditioned by either wild type or benzoxazinoid-deficient bx1 maize mutant plants. Following maize cultivation, we detected benzoxazinoid-dependent chemical and microbial fingerprints in the soil. The benzoxazinoid fingerprint was still visible during wheat growth, but the microbial fingerprint was no longer detected. Wheat emergence, tillering, growth, and biomass increased in wild type conditioned soils compared to bx1 mutant conditioned soils. Weed cover was similar between soil conditioning treatments, but insect herbivore abundance decreased in benzoxazinoid-conditioned soils. Wheat yield was increased by over 4% without a reduction in grain quality in benzoxazinoid-conditioned soils. This improvement was directly associated with increased germination and tillering. Taken together, our experiments provide evidence that soil conditioning by plant secondary metabolite producing plants can increase yield via plant-soil feedbacks under agronomically realistic conditions. If this phenomenon holds true across different soils and environments, optimizing root exudation chemistry could be a powerful, genetically tractable strategy to enhance crop yields without additional inputs

Bacterial tolerance to host-exuded specialized metabolites structures the maize root microbiome.

Plants exude specialized metabolites from their roots, and these compounds are known to structure the root microbiome. However, the underlying mechanisms are poorly understood. We established a representative collection of maize root bacteria and tested their tolerance against benzoxazinoids (BXs), the dominant specialized and bioactive metabolites in the root exudates of maize plants. In vitro experiments revealed that BXs inhibited bacterial growth in a strain- and compound-dependent manner. Tolerance against these selective antimicrobial compounds depended on bacterial cell wall structure. Further, we found that native root bacteria isolated from maize tolerated the BXs better compared to nonhost Arabidopsis bacteria. This finding suggests the adaptation of the root bacteria to the specialized metabolites of their host plant. Bacterial tolerance to 6-methoxy-benzoxazolin-2-one (MBOA), the most abundant and selective antimicrobial metabolite in the maize rhizosphere, correlated significantly with the abundance of these bacteria on BX-exuding maize roots. Thus, strain-dependent tolerance to BXs largely explained the abundance pattern of bacteria on maize roots. Abundant bacteria generally tolerated MBOA, while low abundant root microbiome members were sensitive to this compound. Our findings reveal that tolerance to plant specialized metabolites is an important competence determinant for root colonization. We propose that bacterial tolerance to root-derived antimicrobial compounds is an underlying mechanism determining the structure of host-specific microbial communities

Accelerated Aggregation Studies of Monoclonal Antibodies: Considerations for Storage Stability

Aggregation of mAbs is a crucial concern with respect to their safety and efficacy. Among the various properties of protein aggregates, it is emerging that their size can potentially impact their immunogenicity. Therefore, stability studies of antibody formulations should not only evaluate the rate of monomer loss but also determine the size distribution of the protein aggregates, which in turn depends on the aggregation mechanism. Here, we study the aggregation behavior of different formulations of 2 monoclonal immunoglobulins (IgGs) in the temperature range from 5 C to 50 C over 52 weeks of storage. We show that the aggregation kinetics of both antibodies follow non-Arrhenius behavior and that the aggregation mechanisms change between 40 C and 5 C, leading to different types of aggregates. Specifically, for a given monomer conversion, dimer formation dominates at low temperatures, while larger aggregates are formed at higher temperatures. We further show that the stability ranking of different molecules as well as of different formulations is drastically different at 40 C and 5 C while it correlates better between 30 C and 5 C. Our findings have implications for the level of information provided by accelerated aggregation studies with respect to protein stability under storage conditionsstatus: Published onlin

Relative qPCR to quantify colonization of plant roots by arbuscular mycorrhizal fungi

Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses

Glyphosate and terbuthylazine effects on soil functions, microbiome composition and crop performance

Herbicides are widely used for weed control in agriculture, though their fate and impact on non-target organisms like soil microbes and their function remain relatively unknown. A further complication is that herbicide effects vary depending on how they are applied and due to varying soil moisture conditions. In this study we tested the hypothesis that spraying glyphosate or terbuthylazine directly onto bare soil and when soil moisture is high would impact the soil microbial communities and their function most strongly. We measured similar amounts of glyphosate and terbuthylazine in soil whether the herbicides were directly applied to soil or first sprayed on the weed Chenopodium album and we found evidence for more rapid metabolization at high soil moisture. We found that the soil bacterial rather than the fungal community was mainly affected by a single application of the two tested herbicides. The identified shifts in community composition were independent of the modes of herbicide application but strongly dependent on soil moisture. We further found that herbicide applications only had a small impact on soil microbial function, which was approximated with analyses of the activities of N-β-acetylglucosaminidase, acid phosphatase and β-glucosidase enzymes in soil. Finally, we also assessed the postapplication performance of the subsequent crop and found that the herbicides did not affect maize height, chlorophyll content and biomass. Overall, our study revealed that a single application of herbicides in recommended doses had minor effects on the soil microbiome with a temporal and soil moisture dependency. The latter finding points out that to avoid repercussions on non-target organisms and soil function, key research needs to solve the context-dependency of rapid herbicide degradation in soil

Relative qPCR to quantify colonization of plant roots by arbuscular mycorrhizal fungi

Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses