Soil metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA498805 | ENA |
REPOSITORIES: ENA
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Project description:Metagenome data from soil samples were collected at 0 to 10cm deep from 2 avocado orchards in Channybearup, Western Australia, in 2024. Amplicon sequence variant (ASV) tables were constructed based on the DADA2 pipeline with default parameters.
2025-06-15 | GSE298403 | GEO
Project description:Metaproteome analysis of a forest soil and a potting soil. Different protein extraction methods were compared to investigate protein extraction efficiency and compatibility with sample downstream processing.
2015-03-26 | PRD000542 | Pride
Project description:Non-targeted LC-MS/MS from Dissolved Organic Matter from soil water from Amazon rain-forest.
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Project description:Soil microbial community is a complex blackbox that requires a multi-conceptual approach (Hultman et al., 2015; Bastida et al., 2016). Most methods focus on evaluating total microbial community and fail to determine its active fraction (Blagodatskaya & Kuzyakov 2013). This issue has ecological consequences since the behavior of the active community is more important (or even essential) and can be different to that of the total community. The sensitivity of the active microbial community can be considered as a biological mechanism that regulates the functional responses of soil against direct (i.e. forest management) and indirect (i.e. climate change) human-induced alterations. Indeed, it has been highglihted that the diversity of the active community (analyzed by metaproteomics) is more connected to soil functionality than the that of the total community (analyzed by 16S rRNA gene and ITS sequencing) (Bastida et al., 2016). Recently, the increasing application of soil metaproteomics is providing unprecedented, in-depth characterisation of the composition and functionality of active microbial communities and overall, allowing deeper insights into terrestrial microbial ecology (Chourey et al., 2012; Bastida et al., 2015, 2016; Keiblinger et al., 2016). Here, we predict the responsiveness of the soil microbial community to forest management in a climate change scenario. Particularly, we aim: i) to evaluate the impacts of 6-years of induced drought on the diversity, biomass and activity of the microbial community in a semiarid forest ecocosystem; and ii) to discriminate if forest management (thinning) influences the resistance of the microbial community against induced drought. Furthermore, we aim to ascertain if the functional diversity of each phylum is a trait that can be used to predict changes in microbial abundance and ecosystem functioning.
2021-02-02 | PXD005447 | Pride
Project description:Forest soil microbial communities from Amazon forest - 2010 replicate II A01 metagenome
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Project description:Forest soil microbial communities from Amazon forest - 2010 replicate II A1 metagenome
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Project description:Forest soil microbial communities from Amazon forest - 2010 replicate II A001 metagenome
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Project description:It has long been recognized that species occupy a specific ecological niche within their ecosystem. The ecological niche is defined as the number of conditions and resources that limit species distribution. Within their ecological niche, species do not exist in a single physiological state but in a number of states we call the Natural Operating Range. In this paper we link ecological niche theory to physiological ecology by measuring gene expression levels of collembolans exposed to various natural conditions. The soil-dwelling collembolan Folsomia candida was exposed to 26 natural soils with different soil characteristics (soil type, land use, practice, etc). The animals were exposed for two days and gene expression levels were measured. The main factor found to regulate gene expression was the soil type (sand or clay), in which 18.5% of the measured genes were differentially expressed. Gene Ontology analysis showed animals exposed to sandy soils experience general stress, affecting cell homeostasis and replication. Multivariate analysis linking soil chemical data to gene expression data revealed that soil fertility influences gene expression. Land-use and practice had less influence on gene expression; only forest soils showed a different expression pattern. A variation in gene expression variation analysis showed overall low variance in gene expression. The large difference in response to soil type was caused by the soil physicochemical properties where F. candida experiences clay soils and sandy soils as very different from each other. This collembolan prefers fertile soils with high organic matter content, as soil fertility was found to correlate with gene expression and animals exposed to sandy soils (which, in general, have lower organic matter content) experience more general stress. Finally, we conclude that there is no such thing as a fixed physiological state for animals in their ecological niche and the boundary between the ecological niche and a stressed state depends on the genes/pathways investigated.
2011-02-15 | GSE21213 | GEO