Project description:Genome-wide transcriptional profiling showed that reducing gravity levels in the International Space Station (ISS) causes important alterations in Drosophila gene expression intimately linked to imposed spaceflight-related environmental constrains during Drosophila metamorphosis. However, simulation experiments on ground testing space-related environmental constraints, show differential responses. Curiously, although particular genes are not common in the different experiments, the same GO groups including a large multigene family related with behavior, stress response and organogenesis are over represented in them. A global and integrative analysis using the gene expression dynamics inspector (GEDI) self-organizing maps, reveals different degrees in the responses of the transcriptome when using different environmental conditions or microgravity/hypergravity simulation devices These results suggest that the transcriptome is finely tuned to normal gravity. In regular environmental conditions the alteration of this constant parameter on Earth can have mild effects on gene expression but when environmental conditions are far from optimal, the gene expression is much more intense and consistent effects.
Project description:Total bacterial DNA was isolated from water and sediment samples from a local watershed and 16S rRNA sequences were analyzed using the Illumina MiSeq v3 platform in order to generate snapshots of bacterial community profiles.
Project description:Bacteria transform nutrients and degrade organic matter, making them an essential part of healthy ecosystems. By assaying bacterial physiology within a complex system, the status of the whole ecosystem can be investigated. Proteins are the dynamic molecules that control essential bacterial physiological responses and those of every organism; characterizing an organism's proteome can therefore provide information on its interaction with the environment. Data dependen proteomic analysis (DDA) is a global approach to assay the entire proteome, but sample complexity and the stochastic nature of mass spectrometry can make it difficult to detect low abundance proteins. We explored the development of targeted proteomic (selected reaction monitoring, SRM) assays in complex ocean samples in order to detect specific bacterial proteins of interest and to assess new tools for mixed community metaproteomic exploration. A mixed community was created from a dilution series of isolated culture of bacteria (Ruegeria pomoeroyi) and phytoplankton (Thalassiosira pseudonana). Using SRM, we were able to detect bacterial peptides from the community that were undetectable with the standard DDA approach. We demonstrate benefits and drawbacks of different proteomic approaches that can be used to probe for and resolve nuances of bacterial physiological processes in complex environmental systems.
Project description:Bacteria transform nutrients and degrade organic matter, making them an essential part of healthy ecosystems. By assaying bacterial physiology within a complex system, the status of the whole ecosystem can be investigated. Proteins are the dynamic molecules that control essential bacterial physiological responses and those of every organism; characterizing an organism's proteome can therefore provide information on its interaction with the environment. Data dependen proteomic analysis (DDA) is a global approach to assay the entire proteome, but sample complexity and the stochastic nature of mass spectrometry can make it difficult to detect low abundance proteins. We explored the development of targeted proteomic (selected reaction monitoring, SRM) assays in complex ocean samples in order to detect specific bacterial proteins of interest and to assess new tools for mixed community metaproteomic exploration. A mixed community was created from a dilution series of isolated culture of bacteria (Ruegeria pomoeroyi) and phytoplankton (Thalassiosira pseudonana). Using SRM, we were able to detect bacterial peptides from the community that were undetectable with the standard DDA approach. We demonstrate benefits and drawbacks of different proteomic approaches that can be used to probe for and resolve nuances of bacterial physiological processes in complex environmental systems.
Project description:Total bacterial DNA was isolated from water and sediment samples from a local watershed and 16S rRNA sequences were analyzed using the Illumina MiSeq v3 platform in order to generate snapshots of bacterial community profiles. A total of 56 samples were collected that represent water and sediment samples from 14 sample sites over two different time points (November 18 and 25, 2011).
Project description:Fire is a crucial event regulating the structure and functioning of many ecosystems. Yet few studies focused on how fire affects both the taxonomic and functional diversity of soil microbial communities, along with plant diversity and soil carbon (C) and nitrogen (N) dynamics. Here, we analyze these effects for a grassland ecosystem 9-months after an experimental fire at the Jasper Ridge Global Change Experiment (JRGCE) site in California, USA. Fire altered soil microbial communities considerably, with community assembly process analysis indicating that environmental selection pressure was higher in burned sites. However, a small subset of highly connected taxa were able to withstand the disturbance. In addition, fire decreased the relative abundances of most genes associated with C degradation and N cycling, implicating a slow-down of microbial processes linked to soil C and N dynamics. In contrast, fire stimulated plant growth, likely enhancing plant-microbe competition for soil inorganic N. To synthesize our findings, we performed structural equation modeling, which showed that plants but not microbial communities were responsible for the significantly higher soil respiration rates in burned sites. In conclusion, fire is well-documented to considerable alter the taxonomic and functional composition of soil microorganisms, along with the ecosystem functioning, thus arousing feedback of ecosystem responses to affect global climate.
Project description:Environmental meta-omics is rapidly expanding as sequencing capabilities improve, computing technologies become more accessible, and associated costs are reduced. The in situ snapshots of marine microbial life afforded by these data provide a growing knowledge of the functional roles of communities in ecosystem processes. Metaproteomics allows for the characterization of the dynamic proteome of a complex microbial community. It has the potential to reveal impacts of microbial metabolism on biogeochemical transport, storage and cycling (for example, Hawley et al., 2014), while additionally clarifying which taxonomic groups perform these roles. Previous work illuminated many of the important functions and interactions within marine microbial communities (for example, Morris et al., 2010), but a review of ocean metaproteomics literature revealed little standardization in bioinformatics pipelines for detecting peptides and inferring and annotating proteins. As prevalence of these data sets grows, there is a critical need to develop standardized approaches for mass spectrometry (MS) proteomic spectrum identification and annotation to maximize the scientific value of the data obtained. Here, we demonstrate that bioinformatics decisions made throughout the peptide identification process are as important for data interpretation as choices of sampling protocol and bacterial community manipulation experimental design. Our analysis offers a best practices guide for environmental metaproteomics.
Project description:Salicylic acid (SA) has long been implicated in plant responses to oxidative stress. A direct assessment of SA effects in planta has been difficult, as SA-overproducing Arabidopsis mutants are compromised in growth or other developmental processes. We now report that transgenic Populus expressing a bacterial bifunctional SA synthase accumulated two to three orders of magnitude more total SA than wild type without affecting growth. Microarray experiments were performed to gauge the transcriptional responses of young source leaves to SA manipulation and/or heat stress. Differentially expressed genes due to SA perturbation or temperature treatment were identified. Co-expression network analysis was performed to identify key driver genes in SA-modulated response.