Dataset Information

High Resolution Assessment of Spatio-Temporal Changes in O2 Concentration in Root-Pathogen Interaction.

ABSTRACT: Fusarium wilt, caused by the fungus Fusarium oxysporum f. sp. lycopersici (Fol), is one of the most destructive soil-borne diseases of tomatoes. Infection takes place on the roots and the process starts with contact between the fungus and the roots hairs. To date, no detailed studies are available on metabolic activity in the early stages of the Fol and tomato root interaction. Spatial and temporal patterns of oxygen consumption could provide new insights into the dynamics of early colonization. Here, we combined planar optodes and spatial analysis to assess how tomato roots influence the metabolic activity and growth patterns of Fol. The results shows that the fungal metabolism, measured as oxygen consumption, increases within a few hours after the inoculation. Statistical analysis revealed that the fungus tends to growth toward the root, whereas, when the root is not present, the single elements of the fungus move with a Brownian motion (random). The combination of planar optodes and spatial analysis is a powerful new tool for assessing temporal and spatial dynamics in the early stages of root-pathogen interaction.

SUBMITTER: Rodeghiero M

PROVIDER: S-EPMC6041416 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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High Resolution Assessment of Spatio-Temporal Changes in O2 Concentration in Root-Pathogen Interaction.

Rodeghiero Mirco M Rubol Simonetta S Bellin Alberto A Turco Elena E Molinatto Giulia G Gianelle Damiano D Pertot Ilaria I

Frontiers in microbiology 20180705

Fusarium wilt, caused by the fungus Fusarium oxysporum f. sp. lycopersici (Fol), is one of the most destructive soil-borne diseases of tomatoes. Infection takes place on the roots and the process starts with contact between the fungus and the roots hairs. To date, no detailed studies are available on metabolic activity in the early stages of the Fol and tomato root interaction. Spatial and temporal patterns of oxygen consumption could provide new insights into the dynamics o ...[more]

PMID: 30026738

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Project description:Despite its high ecological importance, the commensal interactions at community level are poorly studied. In tropical dry forests (TDF) there is a great diversity of species adapted to the high seasonality that characterizes them; however, little is known regarding how the spatial and temporal availability of resources generates changes in the pattern of commensal interactions. We experimentally studied changes in the diversity, composition, and pattern of interactions in spatio-temporal associations between the saproxylophagous beetles and their host trees in a TDF in Morelos, Mexico. A total of 65 host tree species were selected, from which 16 wood sections were obtained per species. These sections were exposed in the field to allow oviposition by the cerambycids under four different (spatio-temporal) treatments. We analyzed the network structure and generated indices at species level (i.e., specialization, species strength, and effective partners) and those related to physical characteristics of the wood (hardness and degradation rate) and the cerambycids (body size). In total, 1,323 individuals of 57 species of cerambycids emerged. Our results showed that, independently of the space and time, the network presented a nested and modular structure, with a high specialization degree and a high turnover of cerambycid species and their interactions. In general, we found that the cerambycids are mostly associated with softwood species with a lower decomposition rate of wood, as well as with the most abundant host species. The commensalistic interactions between the cerambycids and their host trees are highly specialized but are not spatio-temporally static. The high turnover in the interactions is caused by the emergence patterns of cerambycids, which seem to restrict their use to certain species. The knowledge of the spatio-temporal variation in Cerambycidae-host tree interactions allows us to predict how environmental and structural changes in the habitat can modify the species ensemble, and therefore its interactions.

Project description:The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Project description:Background The root-associated microbiome has been of keen research interest especially in the last decade due to the large potential for increasing overall plant performance in agricultural systems. Studies about spatio-temporal variation of the root-associated microbiome focused so far primarily on community-compositional changes of annual plants, while little is known about their perennial counterparts. The aim of this work was to get deep insight into the spatial patterns and temporal dynamics of the root associated microbiota of apple trees. Results The bacterial community structure in rhizospheric soil and endospheric root material from orchard-grown apple trees was characterized based on 16S rRNA gene amplicon sequencing. At the small scale, the rhizosphere and endosphere bacterial communities shifted gradually with increasing root size diameter (PERMANOVA R2-values up to 0.359). At the larger scale, bulk soil heterogeneity introduced variation between tree individuals, especially in the rhizosphere microbiota, while the presence of a root pathogen was contributing to tree-to-tree variation in the endosphere microbiota. Moreover, the communities of both compartments underwent seasonal changes and displayed year-to-year variation (PERMANOVA R2-values of 0.454 and 0.371, respectively). Conclusions The apple tree root-associated microbiota can be spatially heterogeneous at field scale due to soil heterogeneities, which particularly influence the microbiota in the rhizosphere soil, resulting in tree-to-tree variation. The presence of pathogens can contribute to this variation, though primarily in the endosphere microbiota. Smaller-scale spatial heterogeneity is observed in the rhizosphere and endosphere microbiota related to root diameter, likely influenced by root traits and processes such as rhizodeposition. The microbiota is also subject to temporal variation, including seasonal effects and annual variation. As a consequence, responses of the tree root microbiota to further environmental cues should be considered in the context of this spatio-temporal variation. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-022-00427-z.

Dataset Information

High Resolution Assessment of Spatio-Temporal Changes in O2 Concentration in Root-Pathogen Interaction.

Publications

High Resolution Assessment of Spatio-Temporal Changes in O<sub>2</sub> Concentration in Root-Pathogen Interaction.

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