Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Environmental functional genes for soil microbes in South East Queensland, Australia

ABSTRACT: Custom made functional gene micoarray (E-FGA) consisting of 13,056 mRNA-enriched anonymus microbial clones from dirverse microbial communities to profile microbial gene transcript in agricultural soils with low and high flux of N2O. A total of 96 genes displayed expression that differed significantly between low and high N2O emitting soils. Creation and validation of an cDNA microarray from environmental microbial mRNA, to use as a monitoring tool for microbial gene expression Microbial expression profiles comparing two high N2O-emitting sites (3 soil replicates and microarrays each) and two low N2O-emitting sites (3 soil replicates and microarray each) from sugarcane site in Mackay, Australia

ORGANISM(S): synthetic construct

SUBMITTER: Regina Sintrajaya

PROVIDER: E-GEOD-23422 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Development of an environmental functional gene microarray for soil microbial communities.

McGrath Ken C KC Mondav Rhiannon R Sintrajaya Regina R Slattery Bill B Schmidt Susanne S Schenk Peer M PM

Applied and environmental microbiology 20100917 21

Functional attributes of microbial communities are difficult to study, and most current techniques rely on DNA- and rRNA-based profiling of taxa and genes, including microarrays containing sequences of known microorganisms. To quantify gene expression in environmental samples in a culture-independent manner, we constructed an environmental functional gene microarray (E-FGA) consisting of 13,056 mRNA-enriched anonymous microbial clones from diverse microbial communities to profile microbial gene ...[more]

PMID: 20851978

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Project description:Soils are a huge reservoir of organic C, and the efflux of CO2 from soils is one of the largest fluxes in the global C cycle. Out of all natural environments, soils probably contain the greatest microbial biomass and diversity, which classifies them as one of the most challenging habitats for microbiologists (Mocali and Benedetti, 2010). Until today, it is not well understood how soil microorganisms will respond to a warmer climate. Warming may give competitive advantage to species adapted to higher temperatures (Rinnan et al., 2009). The mechanisms behind temperature adaptations of soil microbes could be shifts within the microbial community. How microbial communities will ultimately respond to climate change, however, is still a matter of speculation. As a post-genomic approach in nature, metaproteomics allows the simultaneous examination of various protein functions and responses, and therefore is perfectly suited to investigate the complex interplay between respiration dynamics, microbial community architecture, and ecosystem functioning in a changing environment (Bastida et al., 2012). Thereby we will gain new insights into responses to climate change from a microbial perspective. Our study site was located at 910 m a.s.l. in the North Tyrolean Limestone Alps, near Achenkirch, Austria The 130 year-old mountain forests consist of Norway spruce (Picea abies) with inter-spread of European beech (Fagus sylvatica) and silver fir (Abies alba). Three experimental plots with 2 × 2 m warmed- and control- subplots were installed in 2004. The temperature difference between control and warmed plots was set to 4 °C at 5 cm soil depth. Soil was warmed during snow-free seasons. In order to extract proteins from forest soil samples, the SDS–phenol method was adopted as previously described by Keiblinger et al. (2012). Protein extractions were performed from each subplot soil samples. The abundance of protein-assigned microbial phylogenetic and functional groups, were calculated based on the normalized spectral abundance factor (NSAF, Zybailov et al., 2006).

Dataset Information

Environmental functional genes for soil microbes in South East Queensland, Australia

Publications

Development of an environmental functional gene microarray for soil microbial communities.

Similar Datasets

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