Ontology highlight
ABSTRACT:
PROVIDER: PRJNA506287 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8224080_1.fastq.gz | Fastqsanger.gz | |||
| SRR8224080_2.fastq.gz | Fastqsanger.gz | |||
| SRR8224090_1.fastq.gz | Fastqsanger.gz | |||
| SRR8224090_2.fastq.gz | Fastqsanger.gz | |||
| SRR8224100_1.fastq.gz | Fastqsanger.gz |
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Project description:Cropping soils vary in extent of natural suppression of soil-borne plant diseases. However, it is unknown whether similar variation occurs across pastoral agricultural systems. We examined soil microbial community properties known to be associated with disease suppression across 50 pastoral fields varying in management intensity. The composition and abundance of the disease-suppressive community were assessed from both taxonomic and functional perspectives.
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Project description:The response of soil microbial community to climate warming through both function shift and composition reorganization may profoundly influence global nutrient cycles, leading to potential significant carbon release from the terrain to the atmosphere. Despite the observed carbon flux change in northern permafrost, it remains unclear how soil microbial community contributes to this ecosystem alteration. Here, we applied microarray-based GeoChip 4.0 to investigate the functional and compositional response of subsurface (15~25cm) soil microbial community under about one year’s artificial heating (+2°C) in the Carbon in Permafrost Experimental Heating Research site on Alaska’s moist acidic tundra. Statistical analyses of GeoChip signal intensities showed significant microbial function shift in AK samples. Detrended correspondence analysis and dissimilarity tests (MRPP and ANOSIM) indicated significant functional structure difference between the warmed and the control communities. ANOVA revealed that 60% of the 70 detected individual genes in carbon, nitrogen, phosphorous and sulfur cyclings were substantially increased (p<0.05) by heating. 18 out of 33 detected carbon degradation genes were more abundant in warming samples in AK site, regardless of the discrepancy of labile or recalcitrant C, indicating a high temperature sensitivity of carbon degradation genes in rich carbon pool environment. These results demonstrated a rapid response of northern permafrost soil microbial community to warming. Considering the large carbon storage in northern permafrost region, microbial activity in this region may cause dramatic positive feedback to climate change, which is important and necessary to be integrated into climate change models.
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2017-01-10 | GSE92233 | GEO
Project description:To study whether and how soil nitrogen conditions affect the ecological effects of long-term elevated CO2 on microbial community and soil ecoprocess, here we investigated soil microbial community in a grassland ecosystem subjected to ambient CO2 (aCO2, 368 ppm), elevated CO2 (eCO2, 560 ppm), ambient nitrogen deposition (aN) or elevated nitrogen deposition (eN) treatments for a decade. Under the aN condition, a majority of microbial function genes, as measured by GeoChip 4.0, were increased in relative abundance or remained unchanged by eCO2. Under the eN condition, most of functional genes associated with carbon, nitrogen and sulfur cycling, energy processes, organic remediation and stress responses were decreased or remained unchanged by eCO2, while genes associated with antibiotics and metal resistance were increased. The eCO2 effects on fungi and archaea were largely similar under both nitrogen conditions, but differed substantially for bacteria. Coupling of microbial carbon or nitrogen cycling genes, represented by positive percentage and density of gene interaction in association networks, was higher under the aN condition. In accordance, changes of soil CO2 flux, net N mineralization, ammonification and nitrification was higher under the aN condition. Collectively, these results demonstrated that eCO2 effects are contingent on nitrogen conditions, underscoring the difficulty toward predictive modeling of soil ecosystem and ecoprocesses under future climate scenarios and necessitating more detailed studies.
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Project description:To study long-term elevated CO2 and enriched N deposition interactive effects on microbial community and soil ecoprocess, here we investigated soil microbial community in a grassland ecosystem subjected to ambient CO2 (aCO2, 368 ppm), elevated CO2 (eCO2, 560 ppm), ambient nitrogen deposition (aN) or elevated nitrogen deposition (eN) treatments for a decade. There exist antagonistic CO2×N interactions on microbial functional genes associated with C, N, P S cycling processes. More strong antagonistic CO2×N interactions are observed on C degradation genes than other genes. Remarkably antagonistic CO2×N interactions on soil microbial communities could enhance soil C accumulation.
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Project description:Tibet is one of the most threatened regions by climate warming, thus understanding how its microbial communities function may be of high importance for predicting microbial responses to climate changes. Here, we report a study to profile soil microbial structural genes, which infers functional roles of microbial communities, along four sites/elevations of a Tibetan mountainous grassland, aiming to explore potential microbial responses to climate changes via a strategy of space-for-time substitution. Using a microarray-based metagenomics tool named GeoChip 4.0, we showed that microbial communities were distinct for most but not all of the sites. Substantial variations were apparent in stress, N and C cycling genes, but they were in line with the functional roles of these genes. Cold shock genes were more abundant at higher elevations. Also, gdh converting ammonium into urea was more abundant at higher elevations while ureC converting urea into ammonium was less abundant, which was consistent with soil ammonium contents. Significant correlations were observed between N-cycling genes (ureC, gdh and amoA) and nitrous oxide flux, suggesting that they contributed to community metabolism. Lastly, we found by CCA, Mantel tests and the similarity tests that soil pH, temperature, NH4+–N and vegetation diversity accounted for the majority (81.4%) of microbial community variations, suggesting that these four attributes were major factors affecting soil microbial communities. Based on these observations, we predict that climate changes in the Tibetan grasslands are very likely to change soil microbial community functional structure, with particular impacts on microbial N cycling genes and consequently microbe-mediated soil N dynamics.
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Project description:Despite the global importance of forests, it is virtually unknown how their soil microbial communities adapt at the phylogenetic and functional level to long term metal pollution. Studying twelve sites located along two distinct gradients of metal pollution in Southern Poland revealed that both community composition (via MiSeq Illumina sequencing of 16S rRNA genes) and functional gene potential (using GeoChip 4.2) were highly similar across the gradients despite drastically diverging metal contamination levels. Metal pollution level significantly impacted microbial community structure (p = 0.037), but not bacterial taxon richness. Metal pollution altered the relative abundance of specific bacterial taxa, including Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes and Proteobacteria. Also, a group of metal resistance genes showed significant correlations with metal concentrations in soil, although no clear impact of metal pollution levels on overall functional diversity and structure of microbial communities was observed. While screens of phylogenetic marker genes, such as 16S rRNA, provided only limited insight into resilience mechanisms, analysis of specific functional genes, e.g. involved in metal resistance, appeared to be a more promising strategy. This study showed that the effect of metal pollution on soil microbial communities was not straightforward, but could be filtered out from natural variation and habitat factors by multivariate statistical analysis and spatial sampling involving separate pollution gradients.
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