Molecular Insight into Polyaromatic Toxicant Degradation

微生物世界由于其37亿年的进化而多种多样，这为微生物提供了 未被发现的代谢能力的机会，包括污染物降解的机会，以及 检测并恢复该活动。众所周知，超过99%的微生物世界没有 被培养，因此尚未被发现。我们建议探索和恢复两个关键的基因 生物降解步骤中的解毒氯化聚芳族化合物从DNA的 这种未培养的微生物多样性，然后使用本研究的分子标记来帮助 场地评估和污染场地生物降解的定量预测。我们 将还原性脱卤酶和芳香族加氧酶作为关键功能来回收， 它们通常是多氯代二恶英降解的限速步骤， 二苯并呋喃，多氯联苯和多环芳烃（PAHs），超级基金的重点化学品， 我们的研究我们的具体目标是：（1）探索和恢复自然的催化多样性， 开发这些聚芳族化合物的微生物代谢能力的综合概况， (2)利用Burkholderia xenovorans LB 400的基因组序列信息， 降解，研究代谢特征的重要降解这些化学品和（3），发展 基于贝叶斯概率网络的定量诊断工具， 被污染的场地。我们建议使用富集来帮助识别功能活跃的群体， 因此，基因，使用稳定的同位素探测从活性降解群体中回收DNA， 宏基因组文库，以恢复完整的基因和操纵子，并使用高通量PCR筛选， 鉴定具有靶基因家族的克隆。这个项目结合了罗格斯大学 大学科学家在芳香族加氧酶和高通量筛选和宏基因组学与 他在密歇根州立大学的还原脱氯、基因组学和微阵列技术方面拥有专业知识。

The microbial world is diverse owing to its 3.7 billion years of evolution, which provides for both the opportunity of undiscovered metabolic capacity, including that for pollutant degradation, and the challenge of detecting and recovering this activity. It is well known that more than 99% of the microbial world has not been cultured and hence remains undiscovered. We propose to explore and recover genes for two key biodegradative steps in the detoxification of chlorinated polyaromatic compounds from the DNA of this uncultured microbial diversity, and then to use the molecular markers from this study to aid in site assessment and quantitative predictions of biodegradation at contaminated sites. We are targeting the reductive dehalogenases and the aromatic oxygenases as the key functions to recover since they are most often the rate limiting steps in the degradation of the polychlorinated dioxins and dibenzofurans, PCBs and polynuclear aromatic hydrocarbons (PAHs), the Superfund chemicals of focus in our study. Our specific aims are to: (1) explore and recover nature's catalytic diversity with the goal of developing a comprehensive profile of microbial metabolic capabilities for these polyaromatic compounds, (2) use the genome sequence information of Burkholderia xenovorans LB400, the most effective PCB degrader, to study the metabolic features important to the degradation of these chemicals and (3), develop quantitative diagnostic tools based on Bayesian probabilistic networks to predict biodegradation at contaminated sites. We propose to use enrichments to help identify the functionally active populations and hence genes, to use stable isotope probing to recover DNA from the active degrading populations, to use metagenomic libraries to recover the full genes and operons, and to use high throughput PCR screening for identifying clones with the targeted gene families. This project combines the expertise of the Rutgers University scientists in aromatic oxygenases and high throughput screening and metagenomics with the expertise at Michigan State University in reductive dechlorination and genomics and microarray technology.