Functional Analysis and Discovery of Microbial Genes Transforming Metallic and Organic Pollutants: Database and Experimental Tools

Wackett, Lawrence P. ; Ellis, Lynda B.M.

University of Minnesota (United States)

关键词: Genomics;    Catabolism;    Database;    Transformations Genomics;    Enzymes;   

DOI  :  10.2172/834986
RP-ID  :  DOE/ER/63268-1
RP-ID  :  FG02-01ER63268
RP-ID  :  834986

美国|英语

来源: UNT Digital Library

【 摘 要 】

Microbial functional genomics is faced with a burgeoning list of genes which are denoted as unknown or hypothetical for lack of any knowledge about their function. The majority of microbial genes encode enzymes. Enzymes are the catalysts of metabolism; catabolism, anabolism, stress responses, and many other cell functions. A major problem facing microbial functional genomics is proposed here to derive from the breadth of microbial metabolism, much of which remains undiscovered. The breadth of microbial metabolism has been surveyed by the PIs and represented according to reaction types on the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD): http://umbbd.ahc.umn.edu/search/FuncGrps.html The database depicts metabolism of 49 chemical functional groups, representing most of current knowledge. Twice that number of chemical groups are proposed here to be metabolized by microbes. Thus, at least 50% of the unique biochemical reactions catalyzed by microbes remain undiscovered. This further suggests that many unknown and hypothetical genes encode functions yet undiscovered. This gap will be partly filled by the current proposal. The UM-BBD will be greatly expanded as a resource for microbial functional genomics. Computational methods will be developed to predict microbial metabolism which is not yet discovered. Moreover, a concentrated effort to discover new microbial metabolism will be conducted. The research will focus on metabolism of direct interest to DOE, dealing with the transformation of metals, metalloids, organometallics and toxic organics. This is precisely the type of metabolism which has been characterized most poorly to date. Moreover, these studies will directly impact functional genomic analysis of DOE-relevant genomes.