Monitoring Genetic and Metabolic Potential for in situ Bioremediation: Mass Spectrometry

Buchanan, Michelle V.

Oak Ridge National Laboratory

关键词: 63 Radiation, Thermal, And Other Environmental Pollutant Effects On Living Organisms And Biological Materials;    Decision Making;    Dna;    Carbon Tetrachloride;    Mixtures;   

DOI  :  10.2172/827404
RP-ID  :  EMSP-55108
RP-ID  :  827404

美国|英语

来源: UNT Digital Library

【 摘 要 】

A number of DOE sites are contaminated with mixtures of dense non-aqueous phase liquids (DNAPLs) such as carbon tetrachloride, chloroform, perchloroethylene, and trichloroethylene. At many of these sites, in situ microbial bioremediation is an attractive strategy for cleanup, since it has the potential to degrade DNAPLs in situ without the need for pump-and-treat or soil removal procedures, and without producing toxic byproducts. A rapid screening method to determine broad range metabolic and genetic potential for contaminant degradation would greatly reduce the cost and time involved in assessment for in situ bioremediation, as well as for monitoring ongoing bioremediation treatment. The objective of this project was the development of mass-spectrometry-based methods to screen for genetic potential for both assessment and monitoring of in situ bioremediation of DNAPLs. These methods were designed to provide more robust and routine methods for DNA based characterization of th e genetic potential of subsurface microbes for degrading pollutants. Specifically, we sought to (1) Develop gene probes that yield information equivalent to conventional probes, but in a smaller size that is more amenable to mass spectrometric detection, (2) Pursue improvements to matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) methodology in order to allow its more general application to gene probe detection, (3) Increase the throughput of microbial characterization by integrating gene probe preparation, purification, and MALDI-MS analysis. Effective decision-making regarding remediation strategies requires information on the contaminants present and the relevant hydrogeology. However, it also should include information on the relevant bacterial populations present and the biodegradative processes they carry out. For each site at which bioremediation is considered, it is necessary to determine whether sufficient intrinsic degradative capability is present to suggest intrinsic bioremediation as a viable option, or whether a strategy involving addition of specific nutrients is more likely to be successful. In addition, if the existing genetic potential does not include the desired processes, it may be necessary to add external organisms as well as nutrients, which would negatively impact cost and feasibility scenarios. Once a bioremediation strategy is decided upon and initiated, it is important to carry out monitoring of the bacteria and their activities. Real-time data of this type during the treatment process can allow ongoing evaluation to optimize biodegradation, reducing cost and avoiding possible toxic byproducts. Clearly, the development of novel bioremediation technologies and informed decision-making regarding bioremediation as a treatment option will require in-depth information on the bacteria present at each site and the processes they carry out. Currently such information is generated by labor- and time-intensive treatability tests in the laboratory, and these do not generally assess a broad range of metabolic processes. We undertook this project because a rapid screening method to evaluate genetic potential is an important development to reduce costs for implementing in situ bioremediation strategies at DOE sites. At the outset of this project, it was clear that the explosion of information in the DNA sequence database raised the possibility of developing diagnostic DNA signatures for key microbial processes, as a means for assessing genetic potential. The methods developed in our project would be able to take advantage of the growing information on sequences from environmental samples as well as from microbial genome sequencing projects. An increasing number of metabolic functions could be screened as the depth of information available for designing diagnostic sequences increased.