Project description:Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems and perhaps more important is the insensitivity of the biological end points that we have used to assess these impacts. In this paper we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land use practices in the surrounding watershed using advanced machine learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data shows that gill tissues are far more responsive and provide superior discrimination of land use classes than hepatopancreas and that transcript encoding proteins involved in energy productions, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P450. Keywords: Comparative genomics, ecogenomics. Tissue differences, impacts of land use and contaminants on gene expression.
Project description:Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems and perhaps more important is the insensitivity of the biological end points that we have used to assess these impacts. In this paper we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land use practices in the surrounding watershed using advanced machine learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data shows that gill tissues are far more responsive and provide superior discrimination of land use classes than hepatopancreas and that transcript encoding proteins involved in energy productions, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P450. Keywords: Comparative genomics, ecogenomics. Tissue differences, impacts of land use and contaminants on gene expression. Oysters were collected from 11 tidal creeks in Georgia, South Carolina and North Carolina at sites variously impacted by human development. A total of 267 individuals were examined for gene expression profiles in gill and hepatopancreas tissues for a total of 534 arrays. The data were filtered though standard tools and ultimately analyzed using advance machine learning techniques.
Project description:Soil transplant serves as a proxy to simulate climate change in realistic climate regimes. Here, we assessed the effects of climate warming and cooling on soil microbial communities, which are key drivers in EarthM-bM-^@M-^Ys biogeochemical cycles, four years after soil transplant over large transects from northern (N site) to central (NC site) and southern China (NS site) and vice versa. Four years after soil transplant, soil nitrogen components, microbial biomass, community phylogenetic and functional structures were altered. Microbial functional diversity, measured by a metagenomic tool named GeoChip, and phylogenetic diversity are increased with temperature, while microbial biomass were similar or decreased. Nevertheless, the effects of climate change was overridden by maize cropping, underscoring the need to disentangle them in research. Mantel tests and canonical correspondence analysis (CCA) demonstrated that vegetation, climatic factors (e.g., temperature and precipitation), soil nitrogen components and CO2 efflux were significantly correlated to the microbial community composition. Further investigation unveiled strong correlations between carbon cycling genes and CO2 efflux in bare soil but not cropped soil, and between nitrogen cycling genes and nitrification, which provides mechanistic understanding of these microbe-mediated processes and empowers an interesting possibility of incorporating bacterial gene abundance in greenhouse gas emission modeling. Fifty four samples were collected from three soil types (Phaeozem,Cambisol,Acrisol) in three sites (Hailun, Fengqiu and Yingtan) along a latitude with reciprocal transplant; Both with and without maize cropping in each site; Three replicates in every treatments.
Project description:Structure and assembly processes of soil bacterial communities under different land use at karst areas remained poorly understood to date. To address this issue, soil samples from arable land and pristine forest over a karst cave, located in the acid rain impacted area, Hubei province, were collected and subjected to high-throughput sequencing and multivariate statistical analysis.
Project description:The impact of land use change and the composition of soil organic carbon on microbial abundance and bacterial diversity in soils across Europe
Project description:Soil transplant serves as a proxy to simulate climate change in realistic climate regimes. Here, we assessed the effects of climate warming and cooling on soil microbial communities, which are key drivers in Earth’s biogeochemical cycles, four years after soil transplant over large transects from northern (N site) to central (NC site) and southern China (NS site) and vice versa. Four years after soil transplant, soil nitrogen components, microbial biomass, community phylogenetic and functional structures were altered. Microbial functional diversity, measured by a metagenomic tool named GeoChip, and phylogenetic diversity are increased with temperature, while microbial biomass were similar or decreased. Nevertheless, the effects of climate change was overridden by maize cropping, underscoring the need to disentangle them in research. Mantel tests and canonical correspondence analysis (CCA) demonstrated that vegetation, climatic factors (e.g., temperature and precipitation), soil nitrogen components and CO2 efflux were significantly correlated to the microbial community composition. Further investigation unveiled strong correlations between carbon cycling genes and CO2 efflux in bare soil but not cropped soil, and between nitrogen cycling genes and nitrification, which provides mechanistic understanding of these microbe-mediated processes and empowers an interesting possibility of incorporating bacterial gene abundance in greenhouse gas emission modeling.