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

肯尼斯·F·里尔登科罗拉多州立大学0329514评估蛋白质组学作为改善生物废物处理过程性能的工具(TSE03-N；探索性提案)工程环境生物过程(如废水处理、原位生物修复)几乎完全依赖微生物群落。然而，这些群落的复杂性可能会使根据微生物参数适当地设计、控制和排除这些过程变得非常困难；相反，必须监测观察到的化学变化并从中推断生物因素。这可能会导致较差的控制和故障诊断，并且不允许对所涉及的微生物联合体进行优化。在过去的20年里，基于核酸的方法的使用已被证明对了解微生物群落的某些方面是有价值的。然而，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系统发育，而不是它的功能，而后者对于工程应用要重要得多。这个探索性项目的目标是获得通过模型废物处理生物反应器的蛋白质组学分析可以获得的数据类型和用途的初步证据。具体目标是(1)展示从混合培养样本中获得二维电泳图的能力，(2)使用计算机科学的模式识别技术来确定是否可以识别重大过程变化的蛋白质标记，以及(3)将这些蛋白质组数据与16S rDNA群体分析和化学浓度进行比较。项目目标将通过分析模型生物处理系统的样品来实现：混合培养生物反应器降解受到不同环境扰动(饲料组成、pH和毒素)的有机物混合物，并对样品进行蛋白质组图谱、种群组成和有机化学组成的分析。这项研究具有重要的意义和智力价值，因为它将对环境生物过程分析的新方法进行首次评估，并有可能揭示有关微生物联合体的功能的新信息，以便与其一起改进生物过程。

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.