Proteomic Analysis of a Bacterial Consortium during Biodegradation of Chemical Mixtures (TSE-03N)

Kenneth F Reardon科罗拉多州立大学0329514评估蛋白质组学作为提高生物废物处理过程性能的工具（TSE 03-N;探索性建议） 工程环境生物过程（例如，废水处理、原位生物修复）几乎完全依赖于微生物群落。然而，这些社区的复杂性使得很难根据微生物参数适当地设计、控制和解决这些过程的问题;相反，必须监测观察到的化学变化，并从中推断出生物因素。这可能导致不良的控制和故障诊断，并且不允许优化所涉及的微生物聚生体。在过去的20年里，使用基于核酸的方法已被证明是有价值的了解微生物群落的某些方面。尽管如此，这些方法有很大的局限性，主要是因为核糖体DNA分析揭示了生物体的遗传学而不是其功能，后者对工程应用更为重要。 这个探索性项目的目标是获得初步证据的类型和效用的数据，可以通过蛋白质组学分析的模型废物处理生物反应器。具体目标是（1）证明从混合培养物样品中获得二维电泳图像的能力，（2）使用计算机科学的模式识别技术来确定是否可以识别出显著过程变化的蛋白质标记物，以及（3）将这些蛋白质组学数据与16 S rDNA群体分析和化学浓度进行比较。该项目的目标将通过分析来自生物处理系统模型的样本来实现：混合培养生物反应器降解受到不同环境扰动（饲料成分，pH值和毒素）的有机物混合物，以及分析蛋白质组谱，人口组成和有机化学成分的样本。 这项研究的意义和智力价值，因为它将产生一个新的方法来分析环境生物过程的第一次评估，有可能揭示新的信息的功能的微生物财团，以改善生物过程。

Kenneth F ReardonColorado State University0329514Assessment of Proteomics as a Tool to Improve Performance of Biological Waste Treatment Processes (TSE03-N; Exploratory Proposal) Engineered environmental bioprocesses (e.g., wastewater treatment, in situ bioremediation) rely almost exclusively on microbial communities. However, the complexity of these communities can make it very difficult to appropriately design, control, and trouble-shoot those processes based on microbial parameters; instead, observed chemical changes must be monitored and biological factors inferred from them. This can lead to poor control and fault diagnosis, and does not allow optimization of the microbial consortium involved. Over the past 20 years, the use of nucleic acid-based methods has proven to be valuable to understand certain aspects of microbial communities. Nonetheless, these methods have significant limitations, primarily because ribosomal DNA analysis reveals an organism's phylogeny instead of its function, and the latter is far more important for engineering applications. The goal of this exploratory project is to obtain initial evidence of the type and utility of data that can be obtained by a proteomics analysis of a model waste treatment bioreactor. Specific objectives are (1) to demonstrate the ability to obtain 2-dimensional electrophoresis images from mixed culture samples, (2) to use pattern recognition techniques from computer science to determine whether protein markers of significant process changes can be identified, and (3) to compare these proteomic data with 16S rDNA population analysis and chemical concentrations. The project objectives will be achieved by analyzing samples from a model bio treatment system: a mixed-culture bioreactor degrading a mixture of organics that is subjected to different environmental perturbations (feed composition, pH, and toxin), and samples analyzed for proteome profiles, population composition, and organic chemical composition. This research is of significance and intellectual merit because it would produce the first evaluation of a new approach to analysis of environmental bioprocesses, with potential to reveal new information on the function of a microbial consortium with which to improve a bioprocess.