Ontology highlight
ABSTRACT:
PROVIDER: PRJNA564909 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10098500_1.fastq.gz | Fastqsanger.gz | |||
| SRR10098500_2.fastq.gz | Fastqsanger.gz | |||
| SRR10098501_1.fastq.gz | Fastqsanger.gz | |||
| SRR10098501_2.fastq.gz | Fastqsanger.gz | |||
| SRR10098502_1.fastq.gz | Fastqsanger.gz |
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Project description:Comparison of hexachlorocyclohexane (HCH) contaminated soils from Spain with a community-specific microarray. These results are being submitted for publication and represent the first use of microarrays for analysis of soil DNA and the first community-specific microarray design. Keywords: other
2004-11-19 | GSE1985 | GEO
Project description:High Arctic soils have low nutrient availability, low moisture content and very low temperatures and, as such, they pose a particular problem in terms of hydrocarbon bioremediation. An in-depth knowledge of the microbiology involved in this process is likely to be crucial to understand and optimize the factors most influencing bioremediation. Here, we compared two distinct large-scale field bioremediation experiments, located at Alert (ex situ approach) and Eureka (in situ approach), in the Canadian high Arctic. Bacterial community structure and function were assessed using microarrays targeting the 16S rRNA genes of bacteria found in cold environments and hydrocarbon degradation genes as well as reverse-transcriptase real-time PCR targeting key functional genes. Results indicated a large difference between sampling sites in terms of both soil microbiology and decontamination rates. A rapid reorganization of the bacterial community structure and functional potential as well as rapid increases in the expression of alkane monooxygenases and polyaromatic hydrocarbon ring-hydroxylating-dioxygenases were observed one month after the bioremediation treatment commenced in the Alert soils. In contrast, no clear changes in community structure were observed in Eureka soils, while key gene expression increased after a relatively long lag period (1 year). Such discrepancies are likely caused by differences in bioremediation treatments (i.e. ex situ vs. in situ), weathering of the hydrocarbons, indigenous microbial communities, and environmental factors such as soil humidity and temperature. In addition, this study demonstrates the value of molecular tools for the monitoring of polar bacteria and their associated functions during bioremediation. 38 soil samples from two high arctic locations that were contaminated-treated, contaminated or not contaminated followed for up to 4 years
2010-05-15 | E-GEOD-17517 | biostudies-arrayexpress
Project description:High Arctic soils have low nutrient availability, low moisture content and very low temperatures and, as such, they pose a particular problem in terms of hydrocarbon bioremediation. An in-depth knowledge of the microbiology involved in this process is likely to be crucial to understand and optimize the factors most influencing bioremediation. Here, we compared two distinct large-scale field bioremediation experiments, located at Alert (ex situ approach) and Eureka (in situ approach), in the Canadian high Arctic. Bacterial community structure and function were assessed using microarrays targeting the 16S rRNA genes of bacteria found in cold environments and hydrocarbon degradation genes as well as reverse-transcriptase real-time PCR targeting key functional genes. Results indicated a large difference between sampling sites in terms of both soil microbiology and decontamination rates. A rapid reorganization of the bacterial community structure and functional potential as well as rapid increases in the expression of alkane monooxygenases and polyaromatic hydrocarbon ring-hydroxylating-dioxygenases were observed one month after the bioremediation treatment commenced in the Alert soils. In contrast, no clear changes in community structure were observed in Eureka soils, while key gene expression increased after a relatively long lag period (1 year). Such discrepancies are likely caused by differences in bioremediation treatments (i.e. ex situ vs. in situ), weathering of the hydrocarbons, indigenous microbial communities, and environmental factors such as soil humidity and temperature. In addition, this study demonstrates the value of molecular tools for the monitoring of polar bacteria and their associated functions during bioremediation. 38 soil samples from two high arctic locations that were contaminated-treated, contaminated or not contaminated followed for up to 4 years
2010-06-19 | E-GEOD-17532 | biostudies-arrayexpress
Project description:Comparison of hexachlorocyclohexane (HCH) contaminated soils from Spain with a community-specific microarray. These results are being submitted for publication and represent the first use of microarrays for analysis of soil DNA and the first community-specific microarray design. Keywords: other
2010-06-05 | E-GEOD-1985 | biostudies-arrayexpress
Project description:Understanding the bacterial community structure, and their functional analysis for active bioremediation process is essential to design better and cost effective strategies. Microarray analysis enables us to simultaneously study the functional and phylogenetic markers of hundreds of microorganisms which are involved in active bioremediation process in an environment. We have previously described development of a hybrid 60-mer multibacterial microarray platform (BiodegPhyloChip) for profiling the bacterial communities and functional genes simultaneously in environments undergoing active bioremediation process (Pathak et al; Appl Microbiol Biotechnol,Vol. 90, 1739-1754). The present study involved profiling the status of bacterial communities and functional (biodegradation) genes using the developed 60-mer oligonucleotide microarray BiodegPhyloChip at five contaminated hotspots in the state of Gujarat, in western India. The expression pattern of functional genes (coding for key enzymes in active bioremediation process) at these sites was studied to understand the dynamics of biodegradation in the presence of diverse group of chemicals. The results indicated that the nature of pollutants and their abundance greatly influence the structure of bacterial communities and the extent of expression of genes involved in various biodegradation pathways. In addition, site specific factors also play a pivotal role to affect the microbial community structure as was evident from results of 16S rRNA gene profiling of the five contaminated sites, where the community structure varied from one site to another drastically.
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Project description:Bacterial community in arsenic contaminated soil
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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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