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• W2023563558 abstract "At the present time we know little about how microbial communities function in their natural habitats. For example, how do microorganisms interact with each other and their physical and chemical surroundings and respond to environmental perturbations? We might begin to answer these questions if we could monitor the ways in which metabolic roles are partitioned amongst members as microbial communities assemble, determine how resources such as carbon, nitrogen, and energy are allocated into metabolic pathways, and understand the mechanisms by which organisms and communities respond to changes in their surroundings. Because many organisms cannot be cultivated, and given that the metabolisms of those growing in monoculture are likely to differ from those of organisms growing as part of consortia, it is vital to develop methods to study microbial communities in situ. Chemoautotrophic biofilms growing in mine tunnels hundreds of meters underground drive pyrite (FeS2) dissolution and acid and metal release, creating habitats that select for a small number of organism types. The geochemical and microbial simplicity of these systems, the significant biomass, and clearly defined biological-inorganic feedbacks make these ecosystem microcosms ideal for development of methods for the study of uncultivated microbial consortia. Our approach begins with the acquisition of genomic data from biofilms that are sampled over time and in different growth conditions. We have demonstrated that it is possible to assemble shotgun sequence data to reveal the gene complement of the dominant community members and to use these data to confidently identify a significant fraction of proteins from the dominant organisms by mass spectrometry (MS)–based proteomics. However, there are technical obstacles currently restricting this type of proteogenomic analysis. Composite genomic sequences assembled from environmental data from natural microbial communities do not capture the full range of genetic potential of the associated populations. Thus, it is necessary to develop bioinformatics approaches to generate relatively comprehensive gene inventories for each organism type. These inventories are critical for expression and functional analyses. In proteomic studies, for example, peptides that differ from those predicted from gene sequences can be measured, but they generally cannot be identified by database matching, even if the difference is only a single amino acid residue. Furthermore, many of the identified proteins have no known function. We propose that these challenges can be addressed by development of proteogenomic, biochemical, and geochemical methods that will be initially deployed in a simple, natural model ecosystem. The resulting approach should be broadly applicable and will enhance the utility and significance of genomic data from isolates and consortia for study of organisms in many habitats. Solutions draining pyrite-rich deposits are referred to as acid mine drainage (AMD). AMD is a very prevalent, international environmental problem associated with energy and metal resources. The biological-mineralogical interactions that define these systems can be harnessed for energy-efficient metal recovery and removal of sulfur from coal. The detailed understanding of microbial ecology and ecosystem dynamics resulting from the proposed work will provide a scientific foundation for dealing with the environmental challenges and technological opportunities, and yield new methods for analysis of more complex natural communities." @default.
• W2023563558 created "2016-06-24" @default.
• W2023563558 creator A5002596998 @default.
• W2023563558 creator A5045273277 @default.
• W2023563558 creator A5045720453 @default.
• W2023563558 creator A5065638508 @default.
• W2023563558 date "2005-12-01" @default.
• W2023563558 modified "2023-09-23" @default.
• W2023563558 title "Proteogenomic Approaches for the Molecular Characterization of Natural Microbial Communities" @default.
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• W2023563558 doi "https://doi.org/10.1089/omi.2005.9.301" @default.
• W2023563558 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16402891" @default.
• W2023563558 hasPublicationYear "2005" @default.

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