Volatile-mediated suppression of plant pathogens is related to soil properties and microbial community composition

There is increasing evidence that the soil microbial community produces a suite of volatile organic compounds that suppress plant pathogens. However, it remains unknown which soil properties and management practices influence volatile-mediated pathogen suppression. The aim of this study was to relate soil properties to growth suppression of three plant pathogens by soil volatiles. We measured the effect of volatiles emitted from a broad range of agricultural soils on the in vitro growth of the plant pathogenic fungi Rhizoctonia solani and Fusarium oxysporum, and the oomycete Pythium intermedium. Growth suppression of pathogens by soil volatiles could be linked to various soil properties, and some aspects of microbial community composition and field history, using multiple linear regression. Volatile-mediated suppression of mycelial development occurred for each pathogen type, but the magnitude of inhibition differed among soils as well as pathogens. On average R. solani and P. ultimum appeared more sensitive to volatile suppression than F. oxysporum. Suppression of R. solani by volatiles was positively correlated with organic matter content, microbial biomass and proportion of litter saprotrophs in the microbial community, but negatively correlated with pH, microbial diversity (Shannon), and the proportion of Acidobacteria in the community. R. solani, F. oxysporum, and P. intermedium suppression by volatiles was affected by various management practices occurring in the soil's field history, such as reduced tillage, the presence of certain crops in the crop rotation, and the application of solid manure. P. intermedium suppression was also negatively correlated with soil sulphur content. This study identifies pathogen-specific drivers of growth-suppressive volatiles, a critical step in integrating soil volatiles into prediction and management of soil-borne plant diseases

Volatile-mediated suppression of plant pathogens is related to soil properties and microbial community composition

There is increasing evidence that the soil microbial community produces a suite of volatile organic compounds that suppress plant pathogens. However, it remains unknown which soil properties and management practices influence volatile-mediated pathogen suppression. The aim of this study was to relate soil properties to growth suppression of three plant pathogens by soil volatiles. We measured the effect of volatiles emitted from a broad range of agricultural soils on the in vitro growth of the plant pathogenic fungi Rhizoctonia solani and Fusarium oxysporum, and the oomycete Pythium intermedium. Growth suppression of pathogens by soil volatiles could be linked to various soil properties, and some aspects of microbial community composition and field history, using multiple linear regression. Volatile-mediated suppression of mycelial development occurred for each pathogen type, but the magnitude of inhibition differed among soils as well as pathogens. On average R. solani and P. ultimum appeared more sensitive to volatile suppression than F. oxysporum. Suppression of R. solani by volatiles was positively correlated with organic matter content, microbial biomass and proportion of litter saprotrophs in the microbial community, but negatively correlated with pH, microbial diversity (Shannon), and the proportion of Acidobacteria in the community. R. solani, F. oxysporum, and P. intermedium suppression by volatiles was affected by various management practices occurring in the soil's field history, such as reduced tillage, the presence of certain crops in the crop rotation, and the application of solid manure. P. intermedium suppression was also negatively correlated with soil sulphur content. This study identifies pathogen-specific drivers of growth-suppressive volatiles, a critical step in integrating soil volatiles into prediction and management of soil-borne plant diseases.</p

Comparing root exudate collection techniques: An improved hybrid method

1. Plant-microbe interactions are critical for ecosystem functioning and drive rhizosphere processes. Root exudates are an important soil carbon (C) input, as well as a mechanism for communication between plants and rhizosphere microbes, but are notoriously difficult to extract and characterise. Common methods produce either substantial noise from the soil or do not mimic natural systems. Optimising methods for root exudate collection in soil is crucial for advancing our understanding of root-microbe interactions under changing environmental conditions. 2. Hybrid root exudate collection methods, where plants are grown in soil and transferred to hydroponics for exudate collection after root washing, might offer an ecologically relevant alternative to existing approaches. However, this method causes potential root damage as well as osmosis and subsequent leaking of cell contents. Here, we assessed different ‘root recovery’ periods after root washing and before hybrid root exudate collection, by comparing root exudate quantity and quality with both damaged root extracts and with leachates collected from the intact root-soil system. This was done across three common grassland species representing three functional groups. 3. We found that root exudate profiles of the shortest recovery period (0 days) were similar to damaged root extracts and were very high in C. With an increasing period of root recovery, profiles were more similar to leachates collected from the intact root-soil system, and C concentrations decreased. While both hybrid and leachate collection methods separated species by their root exudate profiles, the hybrid method was less variable in terms of the amount of C measured and provided a more diverse and abundant metabolome with better identification of metabolites. 4. Our results show that a recovery period after root washing of at least 3 days is critical to prevent root damage bias in hybrid collection methods, and that our hybrid method yields exudates that discriminate between species. Our data also suggest that exudates collected with this hybrid method are ecologically valid, which is vital for gaining a mechanistic understanding of their role in ecosystem functioning

Are researchers following best storage practices for measuring soil biochemical properties?

It is widely accepted that the measurement of organic and inorganic forms of carbon (C) and nitrogen (N) in soils should be performed on fresh extracts taken from fresh soil samples. However, this is often not possible, and it is common practice to store samples (soils and/or extracts), despite a lack of guidance on best practice. We utilised a case study on a temperate grassland soil taken from different depths to demonstrate how differences in soil and/or soil extract storage temperature (4 or −20 ∘C) and duration can influence sample integrity for the quantification of soil-dissolved organic C and N (DOC and DON), extractable inorganic nitrogen (NH4+ and NO3-) and microbial biomass C and N (MBC and MBN). The appropriateness of different storage treatments varied between topsoils and subsoils, highlighting the need to consider appropriate storage methods based on soil depth and soil properties. In general, we found that storing soils and extracts by freezing at −20 ∘C was least effective at maintaining measured values of fresh material, whilst refrigerating (4 ∘C) soils for less than a week for DOC and DON and up to a year for MBC and MBN and refrigerating soil extracts for less than a week for NH4+ and NO3- did not jeopardise sample integrity. We discuss and provide the appropriate tools to ensure researchers consider best storage practice methods when designing and organising ecological research involving assessments of soil properties related to C and N cycling. We encourage researchers to use standardised methods where possible and to report their storage treatment (i.e. temperature, duration) when publishing findings on aspects of soil and ecosystem functioning. In the absence of published storage recommendations for a given soil type, we encourage researchers to conduct a pilot study and publish their findings.</p

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis

Detecting macroecological patterns in bacterial communities across independent studies of global soils.

The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential 'indicator' taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past